Automated Driving - TUM · TECHNICAL UNIVERSITY OF MUNICH TUM School of Management Thesis Master of...

Benedikt Schwab

Automated Driving:Analysis of Standard-Setting Dynamics and Development of a PedestrianSimulation Model

T E C H N I C A L U N I V E R S I T Y O F M U N I C HTUM School of Management

ThesisMaster of Science

November 30, 2017

A U T O M AT E D D R I V I N GAnalysis of Standard-Setting Dynamics and Development of a Pedestrian

Simulation Model

benedikt schwab

Technical University of MunichSchöller Chair in Technology and Innovation Management

Prof. Dr. Joachim HenkelM. Sc. Lisa Teubner

Technical University of MunichChair of Computational Modeling and Simulation

Prof. Dr.-Ing. André BorrmannDr. rer. nat. Peter Kielar

A B S T R A C T

Automated driving has the potential to disrupt the automotive in-dustry. Realizing this technology will involve various industries andwill lead to the emergence of uniform approaches, which will beadopted by multiple stakeholders from different areas. This includesstandardization areas, such as communication, digital mapping, andminimum quality requirements.

The intention of this work is to systematically analyze the standard-ization dynamics in the areas relevant to automated driving. Further-more, the industry’s key actors are identified and their positioningregarding standards organizations and consortia is reviewed. Hereby,the specification of tests to ensure properly functioning systems con-stitutes one of the most essential standardization topics. The verifica-tion of automated driving systems will include standardized scenariosimulations, which model rural and urban traffic situations.

Since particularly pedestrians are exposed to malfunctioning auto-mated driving systems, a realistic simulation of pedestrian behavioris crucial for the testing of such systems. Therefore, the second partof this work aims at the implementation of a model, which describespedestrian behavior when interacting with cars in urban crossing sce-narios. The driving simulator Virtual Test Drive and the pedestriansimulation framework MomenTUMv2 are used for this purpose.

Z U S A M M E N FA S S U N G

Automatisiertes Fahren hat das Potential die Automobilindustrie dis-ruptiv zu verändern. Die Realisierung dieser Technologie involviertunterschiedliche Industrien und wird zu einer Entwicklung von ein-heitlichen Lösungsansätzen führen, die von zahlreichen Stakeholdernaus unterschiedlichen Bereichen übernommen werden. Dazu gehörenStandardisierungsbereiche wie Kommunikation, digitale Kartierungund qualitative Mindestanforderungen.

Ziel dieser Arbeit ist es, die Standardisierungsdynamiken in denfür das automatisierte Fahren relevanten Bereichen systematisch zuanalysieren. Darüber hinaus werden die Schlüsselakteure der Bran-che identifiziert und ihre Positionierung in Bezug auf Standardisie-rungsorganisationen und Konsortien inspiziert. Eines der wichtigs-ten Standardisierungsthemen ist dabei die Spezifikation von Testszur Gewährleistung ordnungsgemäß funktionierender Systeme. DieFunktionalität automatisierter Fahrsysteme wird durch die Simulati-on von standardisierten Szenarien verifiziert werden, die ländlicheund städtische Verkehrssituationen abbilden.

Da insbesondere Fußgänger der Fehlfunktion von automatisiertenFahrsystemen ausgesetzt sind, ist eine realistische Simulation desFußgängerverhaltens von entscheidender Bedeutung für das Testensolcher Systeme. Der zweite Teil dieser Arbeit zielt daher auf die Im-plementierung eines Modells ab, welches Fußgängerverhalten bei derInteraktion mit Autos in städtischen Kreuzungsszenarien beschreibt.Hierfür werden der Fahrzeugsimulator Virtual Test Drive und das Fuß-gängersimulationsframework MomenTUMv2 verwendet.

A C K N O W L E D G M E N T S

Lisa Teubner,Dr. Peter Kielar,Christoph Sippl,Prof. Dr. Joachim Henkel,Prof. Dr. André Borrmann.

Sarah,Mike.

My parents,my sister,Lisa.

I wish to express my sincere gratitude to you.

C O N T E N T S

1 introduction 1

1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

i standard-setting dynamics

2 standardization theory 5

2.1 Types of Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.1 Quality Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.2 Compatibility Standards . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Market Extent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Standardization Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3.1 Unsponsored Standards . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.2 Sponsored Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.3 Voluntary Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.4 Mandated Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4 Control and Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5 Product Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 compatibility standardization dynamics 19

3.1 Terminilogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Sensors and Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3 Car2X Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.1 Automated Driving Use Cases . . . . . . . . . . . . . . . . . . . . . . 24

3.3.2 IEEE 802.11p / ITS-G5 . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3.3 Mobile Broadband . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4 Navigation and Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 quality standardization dynamics 33

4.1 Technical Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3 European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.4 EU-US-Japan Trilateral Cooperation . . . . . . . . . . . . . . . . . . . . . . . 36

ii pedestrian behavior model

5 literature review 41

5.1 Scales of Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.2 Pedestrian Behavioral Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3 Related Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3.1 Feng et al. 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3.2 Hashimoto et al. 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.3.3 Anvari et al. 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.3.4 Zeng et al. 2014, 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.3.5 Overview of Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6 pedestrian behaviour model 47

x contents

6.1 Strategic Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.2 Tactical Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

6.3 Operational Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6.3.1 Driving Force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.3.2 Conflicting Pedestrian . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.3.3 Conflicting Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

6.3.4 Crosswalk Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7 implementation 55

7.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7.2 Virtual Test Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

7.3 Intermediary Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

7.4 MomenTUMv2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.4.1 Car Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.4.2 Additional Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.4.3 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

7.4.4 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

7.4.5 Tactical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

7.4.6 Operational Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

8 dataset 65

8.1 Ko-PER Intersection Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8.2 Dataset Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

iii concluding discussion

9 discussion 71

9.1 Pedestrian Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

9.1.1 Tactical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

9.1.2 Operational Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

9.2 Standardization Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

10 conclusion and outlook 83

iv appendix

a companies per industry 87

a.1 Automotive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

a.2 Telecommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

a.3 Navigation and Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

b model parameters 91

bibliography 93

L I S T O F F I G U R E S

Figure 2.1 Statistics on the Standards War Betamax–VHS . . . . . . . . . . . . . 5

Figure 2.2 Network Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 2.3 Standards Reinforcement Mechanism . . . . . . . . . . . . . . . . . 13

Figure 3.1 Replacing the Human Driver with Technology . . . . . . . . . . . . 19

Figure 3.2 Protocol Stack of DSRC and C-ITS . . . . . . . . . . . . . . . . . . . . 26

Figure 3.3 Building Blocks of the NDS Specification . . . . . . . . . . . . . . . . 32

Figure 4.1 Generic V Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 4.2 Central Issues of the PEGASUS Project . . . . . . . . . . . . . . . . 35

Figure 4.3 Organization Chart of the Trilateral Cooperation . . . . . . . . . . . 38

Figure 5.1 Distributed Simulation Setup . . . . . . . . . . . . . . . . . . . . . . 41

Figure 5.2 Pedestrians on a Square Lattice . . . . . . . . . . . . . . . . . . . . . 43

Figure 5.3 DBN of the Pedestrian Behavior Model by Hashimoto et al. . . . . . 43

Figure 5.4 Social Force Model for Shared Spaces by Anvari et al. . . . . . . . . 44

Figure 5.5 Signalized Crosswalk of Zeng et al.’s Model . . . . . . . . . . . . . 45

Figure 6.1 Exemplary Intersection with Origin and Destination Areas . . . . . 47

Figure 6.2 Exemplary Pedestrian Crossing with Navigation Graph . . . . . . . 48

Figure 6.3 Potentially Colliding Pedestrians i and j . . . . . . . . . . . . . . . . 50

Figure 6.4 Tangential Force Exerted from Pedestrian j on Pedestrian i . . . . . 51

Figure 6.5 Angle ϕi,j between Pedestrian i and j . . . . . . . . . . . . . . . . . . 52

Figure 6.6 Car k Exerting Repulsive Forces on Pedestrian i and j . . . . . . . . 53

Figure 6.7 Social Forces of Pedestrian i and j on the Crosswalk . . . . . . . . . 53

Figure 7.1 Distributed Simulation Setup . . . . . . . . . . . . . . . . . . . . . . 55

Figure 7.2 Road Network and Scenario Creation with VTD . . . . . . . . . . . 56

Figure 7.3 Structure of MomenTUMv2 . . . . . . . . . . . . . . . . . . . . . . . . 59

Figure 7.4 Layers and Layer Groups of AutoCAD . . . . . . . . . . . . . . . . . 60

Figure 7.5 Possible Intersections of Two Rays . . . . . . . . . . . . . . . . . . . 61

Figure 7.6 Ellipse with Pedestrian at Point x and Normal Vector #„n . . . . . . . 62

Figure 7.7 Segment Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Figure 7.8 3D View of MomenTUMv2’s Visualization Tool . . . . . . . . . . . . 63

Figure 8.1 Public Crossing in Aschaffenburg . . . . . . . . . . . . . . . . . . . . 65

Figure 8.2 Ko-PER Intersection Drawn in AutoCAD . . . . . . . . . . . . . . . 66

Figure 8.3 Trajectories of the Ko-PER Dataset . . . . . . . . . . . . . . . . . . . 67

Figure 9.1 Trajectories of Pedestrians Simulated with µs = 0.2 . . . . . . . . . 72

Figure 9.2 Trajectories of Pedestrians Simulated with µs = 0.3 . . . . . . . . . 73

Figure 9.3 Ko-PER Sequence 1b . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 9.4 Simulated Pedestrians with Different Pedestrian Interaction Forces 75

Figure 9.5 Pedestrians Before Collision . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 9.6 Pedestrian Trajectories of the Ko-PER Dataset . . . . . . . . . . . . 76

Figure 9.7 Trajectories of Simulated Pedestrians Crossing the Street . . . . . . 76

Figure 9.8 Strength of Social Forces Exerted by a Crosswalk on a Pedestrian . 77

Figure 9.9 Pedestrian Crossing without Cars Located Nearby . . . . . . . . . . 77

Figure 9.10 Repulsive Effect of a Starting Car . . . . . . . . . . . . . . . . . . . . 78

Figure 9.11 Strength of Social Forces Exerted on a Pedestrian by a Car . . . . . 79

Figure 9.12 Database Concept for Testing Highly Automated Driving Systems 80

L I S T O F TA B L E S

Table 2.1 Types of Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 2.2 Standard Properties Regarding Market Extent . . . . . . . . . . . . 9

Table 2.3 Standardization Processes . . . . . . . . . . . . . . . . . . . . . . . . 11

Table 2.4 Control and Positioning Regarding Standards . . . . . . . . . . . . 17

Table 2.5 Strategic Positioning in the Standards War Betamax–VHS . . . . . . 17

Table 2.6 Standard Properties Regarding Product Scope . . . . . . . . . . . . 18

Table 3.1 Organizations Involved in Defining and Standardizing the Termi-nology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Table 3.2 Automated Driving Levels . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 3.3 Protocols for Inner Car Communication . . . . . . . . . . . . . . . . 22

Table 3.4 Promoting and Adopting Members of the OPEN Alliance SIG . . . . 23

Table 3.5 Firm Members of the Working Group IEEE P802.11 and the Car-2-Car Communication Consortium . . . . . . . . . . . . . . . . . . . . 26

Table 3.6 SDOs cooperating within 3GPP . . . . . . . . . . . . . . . . . . . . . . 27

Table 3.7 3GPP Releases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 3.8 Member Firms of th SDO Cooperating within the 3GPP and 5GAA . 29

Table 3.9 Overview of Consortia Coordinated by the OADF . . . . . . . . . . . 31

Table 4.1 Members of the PEGASUS Project . . . . . . . . . . . . . . . . . . . 35

Table 4.2 Partners of the European Platform ERTICO and the Consortium VRA 36

Table 4.3 Selection of Identified Standardization Needs by VRA . . . . . . . . 37

Table 5.1 Pedestrian Behavioral Levels . . . . . . . . . . . . . . . . . . . . . . 42

Table 5.2 Overview of Pedestrian Behavior Models . . . . . . . . . . . . . . . 46

Table 7.1 Simulation Data Protocol . . . . . . . . . . . . . . . . . . . . . . . . . 57

Table 7.2 Overview of the MomenTUMv2 Project . . . . . . . . . . . . . . . . . 58

Table 8.1 Number of Objects in Ko-PER Dataset . . . . . . . . . . . . . . . . . 66

Table A.1 Largest Vehicle Manufacturers with Brands . . . . . . . . . . . . . . 87

Table A.2 Largest OEM Parts Suppliers . . . . . . . . . . . . . . . . . . . . . . . 88

Table A.3 Largest Telecommunication Equipment Makers . . . . . . . . . . . 89

Table A.4 Largest Telecommunication Service Providers . . . . . . . . . . . . 89

Table A.5 Map Data Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Table B.1 Parameters Used for Simulations . . . . . . . . . . . . . . . . . . . . 91

L I S T I N G S

Listing 7.1 XML Configuration of the Tactical Model . . . . . . . . . . . . . . . . 63

Listing 7.2 XML Configuration of the Perception Model . . . . . . . . . . . . . . 64

Listing 7.3 XML Configuration of the Walking Model . . . . . . . . . . . . . . . 64

A C R O N Y M S

3D Three-Dimensional

3GPP 3rd Generation Partnership Project

3G Third Generation

4G Fourth Generation

5G 5th Generation Mobile Networks

5GAA 5G Automotive Association

ADASIS Advanced Driver Assistance Systems Interface Specifications

ADTF Automotive Data and Time-Triggered Framework

ANSI American National Standards Institute

ARIB Association of Radio Industries and Businesses

ATIS Alliance for Telecommunications Industry Solutions

BASt Bundesanstalt für Straßenwesen

BTS Base Transceiver Station

C2B Car-to-Backend

C2C Car-to-Car

C2I Car-to-Infrastructure

C2X Car-to-Everything

CAD Computer-Aided Design

CAN Controller Area Network

CCSA China Communications Standards Association

xiv acronyms

CD Compact Disk

CEN Comité Européen de Normalisation

C-ITS Cooperative Intelligent Transportation System

CSMA/CA Carrier Sense Multiple Access/ Collision Avoidance

CSV Comma-Separated Values

D2D Device-to-Device

DBN Dynamic Bayesian Network

DIN Deutsches Institut für Normung

DSK Dvorak Simplified Keyboard

DSRC Dedicated Short Range Communication

ESO European Standardization Organization

ETSI European Telecommunications Standards Institute

EU European Union

GAAP Generally Accepted Accounting Principles

GUI Graphical User Interface

HDMI High-Definition Multimedia Interface

HTML Hypertext Markup Language

IEEE Institute of Electrical and Electronics Engineers

IETF Internet Engineering Task Force

IFRS International Financial Reporting Standards

ISO International Organization for Standardization

ITS Intelligent Transportation Systems

JSON JavaScript Object Notation

LAN Local Area Networking

LIN Local Interconnect Network

LTE Long Term Evolution

MOST Media Oriented Systems Transport

NDS Navigation Data Standard

NHTSA National Highway Traffic Safety Administration

acronyms xv

NSB National Standards Body

OADF Open AutoDrive Forum

OD Origin Destination

OEM Original Equipment Manufacturer

OPEN One-Pair Ether-Net

OSI Open Systems Interconnection

PC Personal Computer

PDF Portable Document Format

RDB Runtime Data Bus

SAE Society of Automotive Engineers

SCP Simulation Control Protocol

SDO Standards Developing Organization

SIG Special Interest Group

SMB Small and Medium-sized Businesses

SSO Standards Setting Organization

TSDSI Telecommunications Standards Development Society

TTA Telecommunications Technology Association

TTC Telecommunication Technology Committee

UE User Equipment

USA United States of America

USB Universal Serial Bus

US United States

VANET Vehicular Ad Hoc Networks

VCR Video Cassette Recording

VHS Video Home System

VRA Vehicle and Road Automation

VTD Virtual Test Drive

W3C World Wide Web Consortium

WLAN Wireless Local Area Networking

XML Extensible Markup Language

1I N T R O D U C T I O N

The introduction of more highly automated drivingsystems, especially with the option of automated collision

prevention, may be socially and ethically mandated if itcan unlock existing potential for damage limitation.

Ethics Commission on Automated and

Connected Driving [Eth17, p. 11]

The safety of all road users can be improved by deploying highlyautomated driving systems. In order to investigate ethical guidelinesregarding automated driving, an Ethics Commission was appointedby a German Federal Ministry and involved experts representing var-ied fields, such as jurisprudence, philosophy, and social sciences. ThisCommission concluded, that the introduction of such systems maybe even ethically mandated, if a positive balance of risks is achieved[Eth17, p. 7]. This raises the central question of how collision preven-tion can be ensured, when automated driving systems are confrontedwith real traffic situations. Clearly, the testing and the safety verifica-tion of such systems are of fundamental importance, as their deploy-ment is only justifiable, if these systems are less harmful than humandrivers [Eth17, p. 10].

However, the testing procedures have to be capable of ensuringsafety for a vast number of traffic scenarios and traffic states. Thisis eminently challenging for urban scenarios, as the number of influ-encing factors and decision-taking participants increases even morecompared to highway and rural situations. Hereby, the simulation ofa vehicle’s environment constitutes an approach to test the perfor-mance of a system by exposing it to a high number of controlled sce-narios. Since pedestrians are particularly vulnerable to inaccuratelyfunctioning driving systems, a realistic pedestrian simulation can beof significant importance in the testing of automated driving systems.

The safety verification of automated driving systems in a multitudeof scenarios is clearly not a manufacturer-specific concern, but rathera macrosocial question, as all road users are exposed to the risksinvolved. Since the German basic law grants every person the rightto life and physical integrity (Article 2 Grundgesetz), the state has theduty to ensure that the driving systems of each manufacturer fulfillsafety requirements. Therefore, testing methods and procedures haveto be standardized and the compliance with such standards has to beaudited. Suitable testing methods are currently under developmentand first standardization initiatives have been formed.

2 introduction

1.1 objectives

The objective of this thesis is to investigate current standardizationapproaches and dynamics in the field of automated driving. This in-cludes not only dynamics for compatibility standardization, but alsostandard-setting dynamics for the assurance of the product’s qual-ity. The second objective of this work is to develop a pedestrian sim-ulation model, which describes the interaction of pedestrians withcars. Thereby, the pedestrian behavior model shall be implementedwithin the pedestrian simulation framework MomenTUMv2, whereasthe cars shall be simulated by the driving simulator Virtual Test Drive.

1.2 outline

This thesis is structured in three parts. Part i involves the analysis ofstandard-setting dynamics regarding the topic of automated driving.Therefore, the theory on the different types of standards and theiremergence processes are explained in Chapter 2. The theory is thenapplied by observing the compatibility standardization dynamics inthe field of automated driving in Chapter 3. Furthermore, Chapter 4

discusses the dynamics of quality standardization efforts to ensureproperly functioning driving systems.

Part ii addresses the development of a pedestrian simulation model.In order to achieve this objective, the literature on pedestrian behav-ior models for intersection scenarios is reviewed in Chapter 5. Subse-quently, Chapter 6 comprises the explanation of the model’s theoret-ical foundation, whereas its implementation details are described inChapter 7. The Ko-PER dataset, which contains recorded trajectoriesof pedestrians and cars, is introduced in Chapter 8.

Part iii concludes the thesis by discussing the conducted work inChapter 9. Finally, Chapter 10 provides a conclusion with an outlookfor future work.

Part I

S TA N D A R D - S E T T I N G D Y N A M I C S

2S TA N D A R D I Z AT I O N T H E O RY

Standards have played a central role in some of the most importantinnovations of recent years. A well-known standards wars started inthe late 1970s and was about the prevailing format on the video cas-sette market. In 1975 Sony introduced the Betamax format, which wasconsidered to be technological more advanced to its competitor. Eventhough JVC’s Video Home System (VHS) format was introduced a Betamax vs. VHS

year later, it succeeded with a 95 % market share eleven years afterits market entrance, as shown in Figure 2.1. The standards strategy

0

20

40

Annu

al P

rodu

ctio

n [m

il]

BetamaxVHS

1976 1978 1980 1982 1984 1986 19880.0

0.5

1.0

Shar

e [%

]

Figure 2.1: Annual production and market share between Betamax and VHS.Based on the statistics by Cusumano et al. [Cus+92, p. 30].

of JVC proved to be superior, as the firm offered quasi open standardspecifications at moderate license fees and actively created an alliancewith other producing firms [Cus+92].

Standards take different forms and are present across most indus-tries. Further products, in which standardization plays a central role,include CDs, DVDs, PCs, and PDFs. However, standards are also presentin non high-tech products, such as razor blades, electrical voltages,typewriter keyboards, and railway networks [Gri95, pp. 1 sq.].

Standardization entails consumer benefits in most cases. This par-ticularly applies to network markets, whereby the telecommunicationmarket constitutes a typical example. The utility and therefore thevalue of a product increases, the more other consumers are partici-pating on the market with compatible products. In contrast to the Value of network

standardstelephone market, in which the value is completely driven by thenumber of users, other products deliver an intrinsic value which is

6 standardization theory

not based on the network. This includes PDF format for example, as itprovides value without others consumers using it. However, the for-mat PDF gains value, when more consumers adopt it due to the facili-tated exchange of digital documents among consumers. Since manu-facturers will often prefer industry standards compared to solutionswith smaller market shares, an industry standard enables a wide va-riety of complementary products. For example, firms will more likelydevelop software for platforms with high market share, which leadsto a higher number of potential customers. This in turn increases thevalue of the original platform [Lem02, pp. 1896 sq.].

Non-network markets can also benefit from standardization. Stan-dardizing product parts can entail a competitive effect, which in turnincreases the quality and decreases the price. By contrast, minimumValue of

non-networkstandards

standards are not facilitating competition, but promoting social wel-fare due to the avoidance of imperfectly informed consumers de-ciding arationally. For example, minimum license standards for doc-tors, lawyers, assure consumers not to hire unqualified help [Lem02,p. 1897].

Several different definitions of the term “standardization” havebeen provided by institutions and researchers. deVries collected, an-deVries’s definition

alyzed and compared existing definitions of organizations, dictionar-ies, experts, international and national standardization organizations.Based on the compared definitions and the practical use of the term“standardization”, deVries derived the following definition:

Standardization is the activity of establishing and record-ing, a limited set of solutions, to actual or potential match-ing problems1,2, directed at benefits for the party or partiesinvolved, balancing their needs, and intending and expect-ing that these solutions will be repeatedly or continuouslyused, during a certain period, by a substantial numberof the parties for whom they are meant. (deVries [deV97,p. 79], 1997)

2.1 types of standards

According to the definition, standards restrict the variety of a poten-tial product to a limited set of solutions. Thereby, some standardsprecisely specify product features, whereas other standards only de-scribe a loose set of characteristics.

1 Matching problems. Problem of determining one or more features of different interre-lated entities in a way that they harmonize with one other or of determining one ormore features of an entity because of its relation(s) with one or more other entities.2

2 Entity. Any concrete or abstract thing that exists, did exist, or might exist, includingassociations among these things. Example: A person, object, event, idea, process, etc.

2.1 types of standards 7

Different types of standards can be identified and are listed inTable 2.1. In general, standards can be categorized into two majorgroups.

Quality standards are concerned with features and characteristics ofthe product itself. Compatibility standards describe the link to otherproducts and services.

Table 2.1: Types of standards [Gri95, p. 22].

Category Type Examples

Quality Minimum attributes

Measurement and grades Packaging, weight and measures

Public regulation Health and safety, trade descriptions

Product characteristics

Style and tastes Fashion, breakfast cereals, brands

Production economies Raw materials, automobiles

Compatibility Complementary products VCR tapes, software, auto parts

Complementary services

Support Maintenance, servicing

Knowledge User training, experience

Direct networks Telephones, railways, LANs

2.1.1 Quality Standards

Quality standards can in turn be further subdivided into product char-acteristics standards and minimum attributes standards. The latter de-scribe measurement requirements for products and minimum qualityspecifications, such as the European Union (EU) energy label, whichdefines energy efficiency levels [EC17]. Further examples include the Minimum attributes

European emission standards [Eur12a] and the EU’s quality standardfor cucumbers, which was revoked in 2009, but is in use nevertheless[Eur88; Han13]. Standards defining minimum attributes of productsand services are often incorporated into legal standards or requiredby government regulations to protect consumers. Consumer protec-tion is achieved, for example, by means of health and safety standardsand facilitate product purchases by reducing searching and transac-tion costs [Gri95, p. 22].

On the contrary, product characteristics standards are loosely definedand describe bundles of features which are similar within a productgroup. Brands can indicate a consistent quality and communicate a Product

characteristicscertain image to the consumer. A firm particularly focused on deliv-ering a special usability experience, will communicate via its brandand image that all products will meet such quality characteristics.


Production economies explain why groups of products and services,generally share a set of features. For example, mobile phones havea common set of product characteristics, as all of them contain a mi-crophone, a speaker, and have almost the same product size. Furtherexamples are cars sharing components and potato crisps having thesame thickness. The main reason why product features have commoncharacteristics is the economical advantage of reducing the develop-ment and manufacturing costs [Gri95, p. 22].

2.1.2 Compatibility Standards

Compatibility standards define the interface requirements to connecta product to its complementary product or create a network of prod-ucts [Gri95, pp. 22 sq.]. The demand for the good car also drives thedemand for it’s complementary good fuel. For this, the fuel has toComplementary

products andservices

fulfill certain product characteristics and therefore conform to com-patibility standards, so that the engine works properly. Complemen-tary services also require compatibility standards. In order to offerrepair services for a car of a special brand, a repair shop might re-quire a specific set of training and tools. Another example constitutesthe prescription of a drug, which requires complementary knowledgeand training.

If core products complement each other and thereby create a di-rect network, interface standards are required to ensure compatibil-ity. For example, the interface standards for mobile phones specifyhow and when radio signals are sent. Furthermore, railway routesDirect networks

require the same electrification systems and the same railway gauge—the distance between rails—to be compatible with each other [Puf92].The value of the network increases by its number of users or railwayroutes.

Not all standards can be attributed to a single category. A car com-ponent, such as a headlamp or car seats, has similar product charac-teristics and has to be compatible to communicate with the controlsystems of a car. In this case, the supply side benefits from produc-tion economies and the demand side benefits or requires compatibil-ity [Gri95, p. 23].

2.2 market extent

Standards can not only be differentiated by their type, but also bytheir appearance on the market. Table 2.2 gives an overview of differ-ent properties classifying a standard with respect to its appearanceon the market.

The dimension group refers to the group, which adopts the stan-dard. Thus, multi-firm standards are adopted by different firms whichproduce similar products [Gri95, p. 24]. For example, “ISO/IEC 7810”

2.2 market extent 9

Table 2.2: Standard properties regarding market extent.

Dimension Property Example

Group Multi-firm ISO/IEC 7810:2003

Multi-product IEEE 802.11

Multi-generation power plugs and sockets

Fragmentation Monolithic QWERTY-Keyboard

Fragmented Bank cards

is a series of standards that specify the physical conditions of iden-tification cards [Int03]. Since multiple banks adopt this standard for Multi-firm

their banking cards, the standard falls under this category. On thecontrary, a corporation can standardize a modular system for theirproducts to achieve economies of scales. A standard of such a mod-ular system, which is only shared to suppliers but not to competingfirms, does not fall under this category.

Standards that are adopted across product lines within a firm aredefined as multi-product standards [Gri95, p. 24]. Most products of aproduct line “mobile phone” will be equipped with a Wireless Lo-cal Area Networking (WLAN) chipset that conforms to the Instituteof Electrical and Electronics Engineers (IEEE) 802.11 standard [Ins17a]. Multi-product

However, a company might adopt national or regional safety stan-dards only for a market-specific derivative of a product, and notacross the whole product line. For example, the United States (US)Code of Federal Regulations defines the standard, that convex mir-rors require the safety warning “Objects in Mirror Are Closer ThanThey Appear” [492].

Multi-generation standards describe standards that are adopted overseveral generations of a product. This category includes standards Multi-generation

for alternating current power plugs and sockets, and the standardspecifying the Universal Serial Bus (USB) data exchange.

Another property of a standard constitutes rivalry standards on themarket. A monolithic standard represents a standard that dominates Monolithic

the industry [Gri95, p. 24]. For example, most Latin-script keyboardsconform to the “QWERTY” layout and Blu-rays have won the formatwar over HD-DVDs [Dav85; CS09].

The bank card market inhabits different standards, such as Master-Card and Visa, and is therefore denoted as fragmented. As markets Fragmented

develop over time, a single standard can prevail out of a market withmultiple standards. Often, standardization is fragmented internation-ally, when countries or regions prefer a certain standard [Gri95, p. 24].This raises the question of why single standards often tend to diffuseand prevail in the market.


Network Effect Theory

The benefits of standardization in network and non-network marketshave already been discussed at the beginning of Chapter 2. The under-lying assumption is referred to as network effect. Katz and Shapirodescribe several sources of positive consumption externalities, whichincrease the consumer’s utility of a product. Such sources includemore readily available product information and the effect, that mar-ket share can signal product quality [KS85, p. 424].

Furthermore, Katz and Shapiro distinguish between direct and in-direct network effects. Direct network effects describe situations inDirect and indirect

network externalities which the utility of a good clearly depends on the number of otherusers [KS85, p. 424]. Indirect network effects, in turn, describe ef-fects where consumers benefit from other users due to interdepen-dencies of complementary goods [Bec06, p. 43]. The latter includesthe hardware-software paradigm: A consumer benefits from otherusers purchasing similar hardware, since the variability and amountof software developed for the hardware will increase [KS85, p. 424].

ui

Expected network size [Ne]

bin

ai

ui

Standard B

Expected network size [Ne]

Standard A

Figure 2.2: Network effects depending on the expected network size.

The relation of a network effect, shown in Figure 2.2, is typicallyexpressed as an utility function of consumer i:

ui(n) = ai + biNe, bi > 0. (2.1)

It is assumed that each consumer buys at most one unit. The utilityconsists of a stand-alone valuation ai and a second term biNe, whichrepresents the expected size of the network. Thereby, N is usuallymeasured in number of compatible units sold and Ne denotes theexpected number. The consumer i individually evaluates the stand-alone valuation and network valuation [MR96, pp. 185 sq.].

2.3 standardization processes

Standards can emerge in various ways. Research on this topic has pro-posed several categories to distinguish between standardization pro-cesses. Standardization processes can generally be divided into two

2.3 standardization processes 11

main categories: De jure standards are formally negotiated and medi-ated by official standards bodies. Standards which are determined bymarket forces are referred to as de facto standards [Gri95, p. 25; FS88,pp. 2 sq.].

However, a more detailed categorization was proposed by Davidand Greenstein and is listed in Table 2.3. According to the definition

Table 2.3: Standardization processes by David and Greenstein [DG90, p. 4].

Group StandardizationProcess

Definition

De facto Unsponsored Unsponsored standards are sets of specifications without anidentified originator having a proprietary interest andwithout a sponsoring agency.

Sponsored Sponsored standards emerge from one or multiple entities,such as suppliers, users or private cooperative ventures,holding a proprietary interest and creating incentives forothers to adopt.

De jure Voluntary Voluntary standards are developed from standards-writingorganizations, but do not have the force of law behindthem.

Mandated Mandated standards emerge from government agencies,which have regulative authority.

of David and Greenstein, unsponsored and sponsored standardiza-tion processes fall under the category of de facto processes. Voluntaryand mandated standardization processes are generally known as dejure processes [DG90, p. 4].

2.3.1 Unsponsored Standards

This type of standardization process has been subject to researchwhich focuses on the economic processes affecting the formation ofunsponsored standards. The category describes situations where noagent has proprietary interest and no entity, such as firms and users,are large enough to influence other entities due to pricing and tech-nology decisions.

A prominent and much cited example for unsponsored standard-ization is the emergence of the QWERTY keyboard layout. Its begin- QWERTY keyboard

layoutning can be traced back to the year 1867, when Christopher LathamSholes invented a primitive writing machine. The machine’s technicalproblems prevented an immediate commercial introduction. In orderto resolve issues of clashing and jamming typebars, the key order-ing was rearranged from an alphabetical order to the QWERTY stan-dard. Moreover, the letters of the brand name “TYPE WRITER” were


placed in one row of the keyboard to impress customers. A series ofuncoordinated decisions followed, which involved typewriter manu-facturers, typing schools, early typists and employers. The interactionof those events lead to the widespread adoption of the QWERTY key-board layout, despite the fact that it is ergonomically inferior to otheralternatives, such as the Dvorak Simplified Keyboard (DSK) [Dav85].

Another example for an unsponsored standardization process con-stitute nuclear power reactors and the used material therefor. In orderto control the energy level of the neutrons in the reactor, a material,named moderator, is used. Further, a coolant material is utilized totransfer the produced heat from the reactor core. The three typesNuclear power

reactors of reactors are distinguished by the used material: light water (H2O),heavy water (D2O) and gas graphite—gas as coolant and graphiteas moderator [Cow90, p. 545]. Despite the serious doubts concerningthe economic and technical superiority of the light water technologyfrom the beginning, a series of circumstances lead to the adoption ofthat technology. Cowan identified three main events attributing therise: In the late 1940s the US Navy chose light water for their propul-sion program and subsequently researched the technology. Second,the explosion of the Soviet bomb was the cause to rush a civilian US

power project, before physicists were ready to make a choice betweenthe technologies. The third event were the decisions of the US andthe European governments to subsidize the light water technology[Cow90, pp. 543,566].

Lock-In by Historical Events

A central question of standardization research is whether a marketcontaining multiple competing standards will stabilize or will lockin to a single standard. Arthur has been one of the first contributingto the economics of standards in 1983, and 1989 respectively. Arthurcompares the competition of unsponsored technologies with increas-ing returns. This describes the phenomenon that complex technolo-Increasing returns

gies often show increasing returns to adoption. The more adoptiona technology achieves, the more experience is gained, which in turnimproves the technology [Art89, p. 116]. Due to the US Navy’s earlyadoption and development efforts into the light water, the technologyhad a head start, when a demand for civilian nuclear power genera-tion emerged [Cow90, p. 541].

Arthur shows that small historical events during early diffusionperiods can cause the economy to gradually lock in towards one tech-nology. The increasing returns technology does not necessarily haveto be superior to the competing technology and, according to Arthur,such small historical events limit the predictability of future marketshares. The dependence on early random and historic events is alsoreferred to as path-dependence [Art89, p. 128].


Bandwagon Coordination Problem

The coordination problem is the following: If a majority adopts acertain standard, the benefits of conforming to it surpass the privatecosts of adoption. However, if the fraction adopting the standard doesnot exceed a critical amount, the consumer’s benefits may not justifythe private costs. Thus, each agent anticipates the decision of others Anticipation of other

agentsand if an important agent publicly commits to a certain standard,others will follow due to knowing that they will be at least compatiblewith the leader [FS88, p. 2]. The ones with the largest private gainswill switch first, later followed by the ones with the largest networkgains. This dynamic process is often referred to as bandwagon effectand is depicted in Figure 2.3 [DG90, p. 9].

Further adoptions

Larger installed base

More complements produced

Greater credibility of standard

Reinforces value to users

Figure 2.3: Standards reinforcement mechanism [Gri95, p. 27].

Further, Farrell and Saloner describe the situation of “excess inertia”where agents are unanimously in favour for a technology, but are onlymoderately willing to start the change. Due to insufficient motivation Excess inertia

and incentives, no agent wants to start the bandwagon process [FS85,p. 72].

2.3.2 Sponsored Standards

David and Greenstein further defines the sponsored standardizationprocess, which also belongs to the group of de facto processes. Itclearly differs from the unsponsored processes, since the agents de-veloping the standards act with proprietary interest. In order to en- Anticipation of

rivalry reactionsable compatibility to complementary goods, sponsoring agents willmanipulate technical standards and pursue price-setting strategies.Therefore, sponsoring agents anticipate possible reactions of rivals, asthis influences the adoption rates of alternative standards. Researchon sponsored standardization processes includes the analysis of thestrategic behavior [DG90, pp. 12 sq.].

A prominent example for a sponsored standardization process con-stitutes the Bluetooth standard. In the early 1990s, multiple firms in-vestigated the possibility to wirelessly connect computers, laptops,mobile phones and other electronics devices with each other.At thattime two major mobile phone manufacturers, Nokia and Ericsson,were developing short-range radio technologies and it became clearthat a standard was required to avoid potential market fragmentation.


Both companies independently approached Intel as potential partnerand then agreed to form an alliance, with a limited number of promot-ers developing the standard. Further, it was decided that firms couldBluetooth

join the alliance as “adopters” and thus receive access to the intellec-tual property at no cost. However, only promoting members are ableto influence and develop the technical specification, adopters have norights to influence the development process. The five firms, Nokia, Er-icsson, Intel, IBM and Toshiba founded the Bluetooth Special InterestGroup (SIG) in 1998. Each firm contributed knowledge from their in-dustry and over 2000 adopter firms joined by the of April 2001 [Kei02,pp. 207 sq.].

Alliances

In order to establish a de facto standard on the market, a majorityof agents have to adopt it. An alliance can facilitate the establish-ment on the market, since it addresses two problems in the processof standardization. First, a technological solution has to be found.Thereby, a small group of standard developers enables a fast deci-sion process and complementary knowledge within that group pro-motes a mutual understanding for each other’s needs. In the case ofBluetooth, the mobile phone,1 the semiconducter,2 and the Personal1 Nokia, Ericsson

2 Intel Computer (PC)3 industry were represented in the promoters group.3 IBM, Toshiba Later four additional firms4 became members of the promoter group,

4 Microsoft, 3Com,Motorola, Lucent

which in turn diversified the interests. It was reported that the largernumber of promoting firms slowed down the compromising and de-cision process [Kei02, pp. 210 sq.].

The second problem addressed by an alliance constitutes the stan-dard diffusion in the market. Alliances can facilitate the triggering ofthe bandwagon effect, since their members provide a larger installedbase, which increases the utility due to network externalities. Fur-thermore, the credibility of a standard is enhanced due to an alliance[Gri95, p. 42].

Strategies

Schilling identified three strategical areas for a firm to manage andpromote the standard diffusion in the market. Usually firms protectinnovation by means of patents, copyrights, secrecy and other mecha-nisms. However, the protection of proprietary technology inhibits theadoption. Thus, a free diffusion and liberal licensing of related tech-Liberal licensing

nology may constitute a strategy to quickly build the installed base.On the other hand, license fees for adopting a standard can pose anincome stream which could finance the standard development [Sch99,pp. 269 sq.]. Proprietary and open standards are further discussed inSection 2.4.


Another option to encourage diffusion is to create contractual ar-rangements with complementary goods providers, and distributors.Such contracts can induce adoption due to price discounts, special ser-vices or advertising assistance. Bundling is also a successful method Complementary

productsto increase the installed base. Thereby, standards piggyback on com-plementary goods, such as the operating system MS-DOS, which wasoriginally bundled with IBM [Sch99, pp. 270 sq.].

Aggressive promotion and marketing can further facilitate the de-ployment. This includes, for example, influencing the perceived in- Promotion and

marketingstalled base by advertising, penetration pricing, and educating con-sumers [Sch99, pp. 271 sq.].

2.3.3 Voluntary Standards

The negotiations and agreements upon de jure standards take placein standards organizations. Here, voluntary standards denote the cir-cumstance that their application is not mandated by law. Such or-ganizations were either founded by private initiatives or are publicagencies created by governments [DG90, p. 24].

Organizations

Standards Developing Organizations (SDOs) are bodies which are con-stituted on a national, regional or international level in order to de-velop and to certify compliance with other standards. SDOs approachthe process of standardization by means of a detailed, structured, andconsensus based process [Haw+17, pp. 4 sq.].

Each country usually has one National Standards Body (NSB), whichis a SDO recognized from the respective country. For example, the National Standards

BodyAmerican National Standards Institute (ANSI) and the Deutsches In-stitut für Normung (DIN) are NSBs of the US and Germany. In order toachieve global harmonization of standards, coordination is organizedby means of the International Organization for Standardization (ISO)[Int15, p. 1].

However, there also exist SDOs, which act on a regional level, suchas the EU. This includes the Comité Européen de Normalisation (CEN),a European Standardization Organization (ESO), which is officiallyrecognized by the EU and brings the European NSBs together [Com17]. European

StandardizationOrganization

Furthermore, the European Telecommunications Standards Institute(ETSI) is also recognized by the EU, whereas the Alliance for Telecom-munications Industry Solutions (ATIS) is its North American counter-part. The telecommunication standards development is globally co-ordinated by the 3rd Generation Partnership Project (3GPP), whichunites seven standards organizations including ETSI and ATIS [Pen15,pp. 25 sqq.; 3rd17c]. National and international standards bodies canbe differentiated by the type of their conflicts. The conflicts on thenational level involve economic interests of firms, whereas the eco-


nomic and political policies of countries or regions are disputed onan international level [DG90, p. 24].

Furthermore, consortia and forums are subsumed under the termStandards Setting Organization (SSO). Similar to SDOs, SSOs also setConsortia and

forums standards, but they do not necessarily conform to the rules of SDO

and are not coordinated within the SDO system [Haw+17, pp. 4 sq.].The World Wide Web Consortium (W3C) and the Internet EngineeringTask Force (IETF) belong to this category and both emerged from aca-demic backgrounds [Sim14, p. 106]. However, the given definitionsare not unambiguous within the literature.

2.3.4 Mandated Standards

The reasons why governments have particular interests in standard-setting are diverse. In order to achieve national or supranational goals,government mandate the adoption of standards [DG90, p. 29]. An ex-National and

supranational goals ample for this constitute food standards due to public health or envi-ronmental reasons. Another example of mandated minimum qualitystandards are security standards regarding the approving of vehicles[Wis15, pp. 4 sqq.]. Moreover, the innovation and industrial competi-tiveness might be fostered by mandated standard-setting.

Further, market coordination problems and network externalitiescan lead to situations in which an intervention by the governmentcan resolve externality problems. An argument for governmental in-tervention are the network effects gained by mandated and broadadoption [DG90, p. 29]. If standards are referred to in laws and reg-ulations, their application can become mandatory. On the one hand,standards can be distinguished by the authorities introducing suchlaws and regulations. On the other hand, standards can be differenti-ated with respect to the entities who have to apply them.

An example for standardization is the compilation of financial re-ports for companies. Since public companies are globally traded onfinancial markets, a uniformed financial reporting standard facilitatestransparency and efficiency. The IFRS Foundation is an internationalInternational

Financial ReportingStandards

and not-for-profit SDO known for its International Financial ReportingStandards (IFRS). Most national jurisdictions mandate domestic pub-lic companies to file their financial report according to IFRS. However,some countries only permit reporting under IFRS and the US mandatesdomestic public companies to report according to Generally AcceptedAccounting Principles (GAAP), which is US specific [IFR17].

2.4 control and positioning

The dimensions access and leadership, which are listed in Table 2.4, re-late to a firm’s strategic decisions concerning a standard. Firms areable to access open standards and the adoption of such is not accom-

2.5 product scope 17

Table 2.4: Control and positioning of firms with regard to standards [Gri95,p. 23].

Dimension Property Example

Access Open HTML

Proprietary HDMI

Leadership Lead Sony (Betamax), JVC (VHS)

Follow Sanyo (Betamax), Matsushita (VHS)

panied with restrictions. An example for an open standard is HTML5

which is developed by the W3C [Wor14]. Firms, SDOs, or other entitieshold property rights over the proprietary standards. If a firm wants Open and

proprietaryto adopt a proprietary standard it may have to pay royalties. Suchstandards are usually protected by means of patents, copyrights, andfirm-specific knowledge. For example, adopters of HDMI have to payannual fees and royalties per units sold. In addition test equipmentservices are offered by the HDMI licensing administrator [HDM17].

Another aspect relates to the positioning of a firm with respect to astandard. One can distinguish between leading firms which actively Leading and

followingcontribute and develop a standard.On the contrary, firms which sim-ply adopt the standard are only following the technological develop-ment of it.

Table 2.5 lists the strategic positioning of the firms which were in-volved in the standard war between Betamax and VHS. As discussedat the beginning of this chapter, Sony and JVC introduced their ownVideo Cassette Recording (VCR) format. Sanyo and Matsushita weremanufacturing companies, which followed the Betamax and VHS for-mat standard [Cus+92].

Proprietary OpenLead Sony (Betamax) JVC (VHS)

Follow Sanyo (Betamax) Matsushita (VHS)

Table 2.5: Strategic positioning in the standards war Betamax–VHS [Gri95,p. 32].

2.5 product scope

The category “product scope” addresses the different dimensions re-garding the ways a product is determined by a standard. An overviewof the dimensions and their properties is given in Table 2.6.

Thereby, the degree refers to the proportion of a product, whichis covered by the standard. The higher the standardization degree, Degree

the less freedom do firms have in designing their product or service.


Table 2.6: Standard characteristics regarding product scope [Gri95, p. 23].

Dimension Property

Degree Significance of standard features

Level Functional layer(s) standardization

Means Built-in

Gateway, converter

The requirements and test methods of Diesel fuel are standardizedby the DIN EN 590 and leave very little room for product differenti-ation [Deu17]. In contrast, a standard describing a modular productplatform will only define the interfaces between parts. Nevertheless,the standard enables a variety of different parts to mix-and-match.

The dimension level refers to the standardization depth: For ex-ample, a compatibility standard can only specify the interface itself.Level

Thereby, the system is treated as a black box and no requirements arelaid down on the inner of the black box. If the compatibility standarddefines certain response times, this may affect the inner componentsof a system [Gri95, p. 24].

The dimension means addresses the question of how compatibilityis achieved. The standard’s technology might either be built-in andMeans

thus the architecture of the product or service conforms to the stan-dard anyway. If this is not the case, an adapter can be utilized tosupport a certain standard [Gri95, p. 24].

3C O M PAT I B I L I T Y S TA N D A R D I Z AT I O N D Y N A M I C S

Standardization will not only be beneficial for automated driving, butalso necessary in some areas. In the following, current standardiza-tion efforts with regard to their influencing firms are discussed.

Automated driving means that the human driver is replaced bytechnology, as shown in Figure 3.1. Therefore, the development tasks Structure

can be structured according to the tasks a human carries out duringdriving. The first four areas represented in Figure 3.1 are discussed

Decision makingcapabilities

Memory

Eyes

Ears

Reflexes/coordinationof movement

Machine learningalgorithms

Maps/environmentalmodels

Sensors

Vehicle to Xcommunication (not

mandatory)

Actuator control

Figure 3.1: Replacing the human driver with technology [Rol16, p. 8].

in the following of this chapter, whereas standardization dynamicsin terms of ensuring safe decision making is analyzed in Chapter 4.However, the term “automated driving” and its standardization ef-forts are examined first.

3.1 terminilogy

In recent years, three major organizations, which are listed in Ta-ble 3.1, have proposed terminology definitions and standards. Theydescribe and structure the capabilities of automated driving systemsinto several levels. The Bundesanstalt für Straßenwesen (BASt) andthe National Highway Traffic Safety Administration (NHTSA) are or-ganizations, which are subordinated to the German government andUS government, respectively. Hereby, the BASt’s mission is to pro-

20 compatibility standardization dynamics

Table 3.1: Organizations which proposed terminology definitions and standards regarding auto-mated driving.

Abbr. Organization Description

BASt Bundesanstalt fürStraßenwesen

The research institute of the German Ministry of Transport and DigitalInfrastructure investigated the legal consequence of increased vehicleautomation in 2012. The institute compiled a set of five automationlevels including partial-, high- and full automation [Gas+12, p. 9].

NHTSA National HighwayTraffic SafetyAdministration

This agency of the US Department of Transportation develops, sets,and enforces federal motor vehicle safety standards. The agencyprovided a description and definition of automation levels [Nat13,pp. 4 sq.].

SAE Society of AutomotiveEngineers

The US-based SDO developed the standard J3016, which was originallypublished in 2014 and revised in 2016 [SAE16a].

vide technical-scientific research and advice for politics, but standard-setting is not included [Bun17]. Contrary to the BASt, the NHTSA hasthe mission to increase traffic safety by developing and setting stan-dards. Furthermore, it has the mandate to enforce standards dueOrganizations

to the Highway Safety Act of 1970 [U.S17]. The Society of Automo-tive Engineers (SAE) is a globally operating society, bringing togetherengineers and scientists from different industries. Moreover, the so-ciety develops standards, such as the standard J3016, which definesfive levels of automated driving. Despite the initial introduction of anown definition, the NHTSA announced to adopt the SAE standard in2016 [SAE16b].

Table 3.2 provides a summary of the proposed levels from eachorganization. Generally, each classification starts at complete manualdriving and moves towards fully automated driving without the re-quirement of human interaction. The key attributes differentiating theautomation levels are based on the question of whether the human orthe system is in charge of monitoring the driving environment. SAE

levels 0–2 contain a gradual responsibility transition from the humandriver to the system for steering, accelerating, and decelerating thevehicle. However, the human driver has always to monitor the envi-ronment and to perform the remaining driving tasks. In contrast, theLevels of automation

human is not obligated to monitor the environment any more, whena level 3 system is active. Nevertheless, the human has to serve asfallback driver, when the system requests to. Level 4 systems are ca-pable of driving and monitoring the environment. Furthermore, thehuman driver is not required as a fallback in certain environments un-der certain conditions, such as motorways for example. At SAE level5 the system can perform the complete driving task from start to endduring all conditions [SAE16a, pp. 22-24].

The definitions of SAE levels 0–3 are very similar to the correspond-ing level definitions of the BASt and the NHTSA. However, the BASt

does not include an automated driving system according to SAE level

3.2 sensors and actuators 21

Table 3.2: SAE’s automated driving levels and their attributes with the corresponding levels ofNHTSA and BASt [SAE14; Smi13].

SAE

levelName Execution of

steering andacceleration/deceleration

Monitoringof drivingenviron-

ment

Fallback ofdynamic

driving task

Systemcapability

Corr.NHTSA

level

Corr.BASt

level

Driver monitors the driving environment

0 No DrivingAutomation

Driver Driver Driver n/a 0 Driveronly

1 DriverAssistance

Driver &System

Driver Driver Somedrivingmodes

1 Assisted

2 PartialDrivingAssistance

System Driver Driver Somedrivingmodes

2 Partiallyauto-

mated

Automated driving system monitors the driving environment

3 ConditionalDrivingAutomation

System System Driver Somedrivingmodes

3 Highlyauto-

mated

4 High DrivingAutomation

System System System Somedrivingmodes

4 Fully au-tomated

5 Full DrivingAutomation

System System System All drivingmodes

4

5, which performs the driving task under all conditions and envi-ronments. Therefore, an automated driving vehicle without steeringwheel is not represented in BASt’s terminology [Gas+12, p. 9]. Whereaslevel 4 of NHTSA is defined as automated driving for an entire tripwithout requiring the human as a fallback, the SAE subdivides this inlevel 4 and 5 depending on whether the system is capable to driveautomated only in certain environments or always.

3.2 sensors and actuators

The environment of an automated vehicle is detected by its sensors.A vehicle’s actuators have to perform the steering and accelerationtasks of the advanced driving system.

In recent years, the internal car communication has increased dueto more information-based application and is expected to further in-crease due to automated driving applications. Therefore, several com-munication protocol standards have been introduced to enable com-patibility between automotive components. However, the complexityincreased also, since nine networking protocols have been specified


[Boa14, p. 36], whereas a selected list is given in Table 3.3. Controller

Table 3.3: Protocols for inner car communication.

Protocol Intro. Attributes [Ixi14, p. 15]

CAN 1983 ISO 11898 Two wires, reliable, inexpensive, relatively lowbandwidth of 1 Mbit s−1, shared medium

LIN 2001 ISO 17987 Single wire, lower cost than CAN, lower bandwidththan CAN

MOST 2001 MOST

CooperationRing architecture of up to 50 Mbit s−1, relatively highbandwidth, high cost

Flexray 2005 ISO 17458 Shared serial bus, higher bandwidth of up to10 Mbit s−1, higher cost, shared medium

Area Network (CAN) was originally developed by Bosch in 1983 andwas specified as an ISO standard in 1993 [Vec16, p. 1]. The system isused for chassis, powertrain, and body electronics. Local InterconnectNetwork (LIN) and Flexray were both originally developed in consor-tia and were transferred to ISO standards. LIN is used for the commu-Status quo

nication establishment to body electronics, such as mirrors or powerseats, and the applications of Flexray include the communication foractive suspension systems and adaptive cruise control. The protocolMedia Oriented Systems Transport (MOST) is only used for cameraand video applications and is maintained by a partnership referredto as MOST Cooperation [MOS17]. Each of those communication pro-tocols is capable of different bandwidths and involves different costs.Therefore, multiple communication systems are deployed in a singlecar for different purposes. This leads to wiring harnesses being thethird heaviest component in cars [Ixi14, p. 7].

However, the traditional networking protocols represent seriouschallenges for the increasing throughput demands of automated driv-ing [Boa14]. In order to resolve the complexity of multiple propri-etary networking standards, new specifications for the application ofEthernet in cars are developed [Ixi14, p. 8]. Ethernet is part of theAutomotive

Ethernet IEEE 802 standard family and is known for its application in the com-puter industry for over 20 years [Hea17]. It operates on the physicaland data link layer of the Open Systems Interconnection (OSI) modeland is in widespread use. Despite its market diffusion, Ethernet wasnot adopted due to not meeting several requirements for automotiveapplication. Electromagnetic interference requirements were not ful-filled, since Ethernet was susceptible to noise. Furthermore, very lowlatencies under 10 µs for fast reacting sensors were not supported.Moreover, Ethernet did not support bandwidth allocation for differ-ent streams and clock synchronization between devices [Ixi14, p. 8].

3.2 sensors and actuators 23

Broadcom initially developed the BroadR-Reach standard, whichallowed for longer distances of copper wiring and met the electro-magnetic interference requirements of the automotive industry [Ixi14,p. 11]. This Ethernet standard operates on the physical layer and uti-lizes an unshielded single twisted pair cable. In order to establish OPEN Alliance SIG

wide scale adoption of Ethernet connectivity, the One-Pair Ether-Net(OPEN) Alliance SIG was formed in 2011 [Bro+11]. Similar to the found-ing story of the Bluetooth SIG, which was discussed in Section 2.3.2,the founding members of the OPEN Alliance consisted of players fromdifferent industries. Thereby, the initial members included companiesfrom the semiconducter industry, such as Broadcom, NXP Semicon-ductors, and Freescale Semiconducters. Furthermore, the automotiveindustry was represented by BMW and Hyundai, whereas Harmanrepresents an automotive supplier focusing on audio and infotain-ment solutions [Bro+11].

Table 3.4: Promoting and adopting members of the OPEN Alliance SIG.

Organization Manufacturer Supplier

OPEN Alliance [OPE17]

Promoters BMW, Daimler, General Motors,Hyundai, Jaguar, Land Rover, Re-nault, Toyota, Volkswagen

Bosch, Continental

Adopters BAIC, Citroën, Fiat Chrysler, Ford,Honda, Hyundai, Mazda, MitsubishiMotors, Nissan, Peugeot, Tata

Aisin, Delphi, Denso,Hyundai Mobis, Lear,Panasonic, Schaeffler,Sumitomo, Valeo, Yazaki

Table 3.4 lists the promoting members, who have the right to influ-ence strategic decisions by participating in the Steering Committee.Both, promoting and adopting members, have the right to contributeto the technical committees of the OPEN alliance [OPE17]. Table 3.4was compiled by analyzing the memberships of the 20 largest auto-motive manufacturers and suppliers, whereas Table A.1 and Table A.2provide a complete list of them.

Clearly, the majority of automotive manufacturers are members ofthe alliance. The European manufacturers are participating in this al-liance without exception, whereas only three Chinese manufacturersand the Japanese manufacturer Suzuki are not members. However,this is somewhat different for the suppliers, since only half of thesuppliers listed in Table A.2 are part of the alliance.

The OPEN Alliance and its standardization efforts belongs to thegroup of sponsored standardization processes. Here, Hyundai andBMW constitute the leaders of this technology, as they are the firstto deploy automotive Ethernet in their cars [Ixi14, p. 4]. Since thisstandard ensures the communication compatibility between devices,


a cooperation on a unified protocol is vital. Furthermore, the stan-dards for vehicle networking is highly fragmented and it is estimatedthat this technology has the potential to reduce 80 % of the costs and30 % of the cabling weight [Ixi14, p. 9].

3.3 car2x communication

As cars will be able to direct theirselves through more and more com-plex traffic scenarios, they will benefit from communicating with theirenvironment. This includes the communication from Car-to-Car (C2C)and from Car-to-Infrastructure (C2I) elements, such as traffic lightsC2C, C2I, C2B

and traffic signs. Furthermore, the term “Car-to-Backend (C2B)” de-scribes the communication to backend systems, which could be traf-fic control centres or vendor specific information servers. C2C, C2I andC2B are often subsumed under the term “Car-to-Everything (C2X)”[Fuc+15, p. 526].

Automated driving does not necessarily require the communica-tion to the car’s environment [5G 15, p. 14]. However, C2X communi-cation enables the car to increase its scope of perception beyond itsown sensed environment. It allows to receive, aggregate, and sharesensed real-time information about traffic events from and with othertraffic participants [Cac+15, p. 89]. Since cooperative behavior andcommunication between different systems requires compatible pro-tocols, efforts are undertaken to agree on joint standards. Use casesin which communication requirements play a central role and stan-dardization efforts towards the enabling of such are discussed in thefollowing.

3.3.1 Automated Driving Use Cases

Overtaking maneuvers on the road are part of the driving routineand performed multiple times during a trip. Overtaking on unidirec-tional roads is less risky compared to two-way roads, since oncom-ing vehicles are always possible and approaching quickly. Therefore,the safety of automated overtaking maneuvers is increased by coop-erative behavior of the involved vehicles. A vehicle might need toAutomated

overtaking increase the gap in front, so that the overtaking vehicle can quicklymerge from the opposed lane into the gap. Fast coordination is par-ticularly important if an oncoming vehicle is approaching. It is esti-mated that such use cases require an end-to-end latency of 10 ms anda reliability, defined as maximum tolerable packet loss rate, of 10−5

[5G 15, p. 30].The second use case describes situations which are close to vehicle

collisions and require communication to resolve the situation in orderto avoid harmful outcomes. When normal traffic control mechanismsCooperative collision

avoidance fail and the planned trajectories lead to a potential collision, the co-

3.3 car2x communication 25

operation between vehicles can prevent and resolve critical situations[Haf+13, p. 2]. In such scenarios the vehicles have to exchange theirtrajectories, assess them, and agree upon a joint strategy to overcomethe situation. The trajectory handshake has to be completed within100 ms with a reliability of 10−5. It is estimated that the exchangedstatus information update should be received within 10 ms and witha probability of 99.9 % [5G 15, p. 30].

Closely spaced vehicle chains on highways are described as high-density platooning. This driving strategy has the potential to reducefuel consumption as a result of decreased air drag and to increase thecapacity of roads due to smoother traffic flow [Och+16, p. 1]. Form- High-density

platooninging and maintaining platoons requires the constant and real-time ex-change of kinematic state information to keep the inter-vehicle dis-tances constantly low. Similar to the other use cases, end-to-end la-tency of 10 ms and a reliability 10−5 is estimated to be required [5G15, p. 30].

3.3.2 IEEE 802.11p / ITS-G5

The SDO IEEE develops and specifies the IEEE 802.11 standard, whichis commonly known as WLAN standard [Ins17a]. Many electrical de-vices, including laptops, mobile phones and tablets, have adoptedthis standard and incorporated the technology. Due to the chipset’ssupport for high data rates and low production cost, IEEE 802.11 repre-sents a universal solution for diverse applications. The standard hasbeen subject to further developments fixing technology issues andadding more functionality. The development of new 802.11 standardsis organized in multiple task groups [CP+13].

In November 2004 the “Task Group p” was founded to developenhancements to the 802.11 standard in order to support IntelligentTransportation Systems (ITS) applications [Ins17a]. The developed ver-sion 802.11p of the standard was affirmed per votes in April 2010 and IEEE 802.11p

then integrated into the IEEE 802.11-2012 under the name VehicularAd Hoc Networks (VANET) [Ins17a; IEE12, p. ix]. A European versionnamed ITS-G5 was derived and adapted to European requirements, ITS-G5

whereas G5 refers to the 5.9 GHz frequency band [Fes14, p. 167]. Fur-thermore, ITS-G5 was specified by the SDO ETSI under the name ETSI

EN 302 663 [Eur12b].The IEEE 802.11p standard comprises an important feature for the

application of the discussed use cases: It allows to establish a directcommunication between source and destination endpoint without re-lying on the coverage of a infrastructure network. This entails two Device-to-

Device (D2D)communication

major advantages: First, no or poor network coverage does not in-hibit C2C and C2I communication. Second, D2D communication signif-icantly decreases the end-to-end latency, since the data is not routedthrough the network infrastructure.


However, the standard entails also several limitations. Due to theutilized medium access strategy Carrier Sense Multiple Access/ Col-lision Avoidance (CSMA/CA), the collision probability increases, as theload of the network increases [Eic07, p. 5]. This means that dense sce-narios, such as crowded intersections in cities, can lead to degradeddata throughput and increased delays. Furthermore, the standard’sprobabilistic nature leads to the limitation that latency, bandwidth,and reliability can not be guaranteed [5G 15, pp. 39,49].

Figure 3.2: Protocol stack and related core standards of DSRC in USA (left) and C-ITS in Europe(right) [Fes15, pp. 411 sq.].

As depicted in Figure 3.2, the IEEE developed a series of 1609 stan-dards named Dedicated Short Range Communication (DSRC) in theUS. Further, the ETSI and CEN proposed another series of standardsfor Cooperative Intelligent Transportation Systems (C-ITSs) in Europe.This includes the GeoNetworking protocol (EN 302 636), which usesgeographical information to address the area in which a packageshould be distributed [CP+13, p. 70].

Table 3.5: Firm members of the working group IEEE P802.11 and the Car-2-Car CommunicationConsortium.

Automotive Telecommunication

Organization Manufacturer Supplier Vendor Operator

IEEE P802.11

[Ins17b]General Motors Panasonic Ericsson, Huawei,

Nokia, Samsung,ZTE

AT&T, DeutscheTelekom, Nippon,Orange

Car-2-CarCommunicationConsortium[CAR17]

Audi, BMW, Ford, Honda,Hyundai, Jaguar, LandRover, MAN, Opel, PSAPeugeot Citroën, GroupeRenault, Toyota,Volkswagen

Bosch,Continental,Delphi, Denso,Valeo

Huawei

Table 3.5 gives an overview of the member firms, which participatein the standardization process of IEEE 802.11. Only one major vehi-cle manufacturer and one supplier are members of the IEEE P802.11

working group. In contrast, all telecommunication equipment ven-IEEE P802.11

dors are present, whereas a complete list is provided in Table A.3.Furthermore, only a limited number of the 20 largest network opera-


tors are part of the SDO’s decision process. However, this suggests thatthe automotive industry is not directly influencing the specificationof the WLAN standard.

The objective of the Car-2-Car Communication Consortium is todevelop and contribute to the C-ITS standard in Europe. Furthermore, Car-2-Car

CommunicationConsortium

it aims at globally harmonizing C2C communication standards andachieves European harmonization by contributing to ETSI’s technicalcommittee for ITS [CAR17]. As listed in Table 3.5, worldwide vehiclemanufacturers are participating in this consortium, whereas Westernmanufacturers are more represented. This relation is also obtainedfor the suppliers, as the Japene supplier Denso constitutes the onlynon-Western exception.

3.3.3 Mobile Broadband

Cellular networks enable mobile phones to access the internet viaBase Transceiver Stations (BTSs), whereas the protocol standardizationplays an essential role, as mobile phones of various vendors have to becompatible with BTSs of different vendors. Thereby, the standardiza-tion process is globally coordinated by the organization 3GPP, which 3GPP

unites seven organization partners [3rd17c]. The partners are telecom-munication SDOs operating on a national or supranational level andare listed in Table 3.6.

Table 3.6: SDOs cooperating within 3GPP [3rd17c].

Organization Abbreviation Country

Association of Radio Industries and Businesses ARIB Japan

Alliance for Telecommunications Industry Solutions ATIS USA

China Communications Standards Association CCSA China

European Telecommunications Standards Institute ETSI Europe

Telecommunications Standards Development Society TSDSI India

Telecommunications Technology Association TTA Korea

Telecommunication Technology Committee TTC Japan

The original aim of the 3GPP was to enhance the development ofThird Generation (3G) mobile communications systems, when it wasfounded in 1998. However, after completing 3G, it continued specify-

Table 3.7: Timelineof 3GPP releases[3rd17a].

Rel. Date

10 Jun. 2011

11 Mar. 2013

12 Mar. 2015

13 Mar. 2016

14 Jun. 2017

15 Sep. 2018 exp.

ing next generations of mobile communication and is currently work-ing on 5th Generation Mobile Networks (5G) [Pen15, p. 33]. The de-velopment process is structured in releases which are approximatelypublished every year or every two years. For example, Release 10 in-troduced Long Term Evolution (LTE)-Advanced and is the first speci-fication which qualifies for Fourth Generation (4G) networks [3rd17c].


Long Term Evolution with Proximity Services

Until Release 12, the standardization development was mainly fo-cused on the communication between BTS and User Equipments (UEs),such as mobile phones or cars. However, a feature named “proximityservices” was published with Release 12, enabling direct D2D commu-nication. This feature is of particular interest for C2C and C2I commu-nication, as it standardizes a direct communication between devicesand bypasses the infrastructure.

The proximity service feature consists of two functions: The dis-covery function enables a UE to discover other UEs that are nearbyvia the LTE air interface and the second function comprises the D2D

communication. Proximity refers not only to the physical distance,but is also dependent on channel conditions, delay and throughput[Lin+14, p. 3; 3rd17b, p. 8]. The resource allocation can be managed inD2D communication

a scheduled mode, where a BTS determines the radio resources, andan autonomous mode, in which the UE selects the radio resource froma pool. However, the proximity service standard also comprises vari-ous limitations including collision risks during resource allocation inautonomous mode, security mechanisms, and slow connection setupprocedures. The latter inhibits low delay C2C and C2I applications asdiscussed Section 3.3.1 [5G 15, pp. 45 sq.].

Fifth Generation

The 5G of communication systems is developed to address the com-munication demands of 2020 and beyond. It can be envisioned asservice-led consolidating of 3G, 4G, WLAN providing greater coverageand reliability. To achieve a true generation shift, a bandwidth of over1 Gbit s−1 and a latency of 1 ms are required. If these objectives are5G targets

achieved, 5G could enable automated driving including the use cases,which were discussed in Section 3.3.1 [GSM14, pp. 9 sq.]. However,the targeted goal of sub-1 ms latency is estimated to be particularlychallenging and might therefore be relaxed. Since 5G is currently un-der development, multiple requirements have been proposed by re-search studies and field tests to achieve and to enhance C2X commu-nications [GSM14; NGM15].

Organizational Overview

Table 3.8 lists the players of the telecommunication and automotiveindustry, whereas complete lists are provided in Appendix A. If an au-tomotive manufacturer has a membership in one of the national andsupranational SDOs, which are united by the 3GPP, then the manufac-turer predominately chooses to participate in his regional SDO. Theexceptions are Hyundai, Mitsubishi, and Toyota, who are not onlymembers of the Japanese and Korean SDOs, but also of the ETSI. A fur-ther exception constitutes the North American manufacturing market,


Table 3.8: Member firms of the SDOs cooperating within the 3GPP and 5GAA.

Automotive Telecommunication

Organization Manufacturer Supplier Vendor Operator

ARIB (Japan)[Ass17]

Honda, Mitsubishi, Nissan,Toyota

Denso,Panasonic,Sumitomo

Ericsson, Huawei,Nokia, Samsung

KDDI, Nippon,Softbank

ATIS (USA)[All17]

Ericsson, Huawei,Nokia, Samsung

AT&T, Verizon

CCSA (China)[Chi17]

Mitsubishi Denso Ericsson, Huawei,Nokia, Samsung,ZTE

China Mobile

ETSI (Europe)[Eur17b]

Audi, Daimler, GroupePSA, Hyundai, Mitsubishi,Renault, Toyota,Volkswagen

Bosch,Continental,Denso, Magna,Panasonic,Valeo

Ericsson, Huawei,Nokia, Samsung,ZTE

AT&T, ChinaTelecom,DeutscheTelekom, Etisalat,Nippon, Orange,Telecom Italia,Telefónica, Telstra,Verizon, Vodafone

TSDSI (India)[Tel17b]

Tata Ericsson, Huawei,Nokia, Samsung

Vodafone

TTA (Korea)[Tel17c]

Hyundai Hyundai Mobis Ericsson, Huawei,Nokia, Samsung

TTC (Japan)[Tel17a]

Mitsubishi, Toyota Denso,Panasonic,Sumitomo

Ericsson, Huawei,Nokia

KDDI, Nippon,Softbank

5GAA

[5G 17]Audi, BAIC, BMW, Ford,Jaguar, Land Rover, SAIC,Volkswagen

Bosch,Continental,Denso,Panasonic,Valeo

Ericsson, Huawei,Nokia, Samsung,ZTE

KDDI, Orange,Softbank,Telefónica, Telstra,Verizon, Vodafone

since neither Ford nor General Motors are members of the listed SDOs.Moreover, no automotive manufacturer and supplier is member of theATIS. Similar to the IEEE 802.11p in Table 3.5, no Chinese automobilemanufacturer is a member of the SDOs. Furthermore, the 5G Automo-tive Association (5GAA)’s target is to bring the telecommunication andautomotive industry together. They follow the mission to harmonizeuse cases and ITS applications by promoting vehicle communicationwithin 5G [5G 17].

Clearly, the IEEE 802.11p standard is in direct competition to thecellular network standards, which are harmonized by 3GPP. Hereby,5G benefits from building upon existing LTE infrastructure, but is es-timated to arrive after 2020. A key enabler for C2X applications isalready standardized and specified in LTE-Advanced. In contrast, the


802.11p standard has been tested for around ten years, but entailstechnical limitations nevertheless [Cor16, p. 12]. This market com-prises high network effects, as the utility of adopting one of the stan-dards is directly dependent on the network size. Furthermore, theadoption of either one of the standards could be exposed to excess in-ertia, since the involved firms do not want to risk potentially wronginvestments.

3.4 navigation and mapping

Digital map data will play a central role in realizing automated driv-ing. The main focus of maps has been to provide navigation servicesand point of interest services. However, the maps have to overcomethree fundamental challenges. First, vehicles have to precisely localizethemselves by matching the sensed information with world referencemap data. A vehicle does not only require the information on whichChallenges

lane it moves, but also needs to acquire the distance to the roadside.Second, a vehicle has to receive information about the environment,which is not in direct sight. This includes traffic conditions, ice onthe road or accidents that lie 10 km ahead. Third, to achieve broadmarket acceptance an enhanced driving experience has to be deliv-ered. Thus, road conditions and changes thereof will influence theautomated driving behavior [RR15, p. 51].

The challenges will require high-definition maps with real-timefunctionality. Since the content of maps and their environment changesover time, frequent map updates will be inevitable. Furthermore, ve-High-definition

maps hicles will send sensed information, such as traffic incidents or mod-ified road sections, back to the map platform. The aggregation ofup-to-date vehicle positions will enable real-time traffic estimationsand the map will enhance its data by the vehicles’ sensors [SH16].

If vehicles obtain information about traffic flows, road construc-tions, accidents, weather, lane closures, etc. and send it to back tothe map, the user’s utility of the map increases by the number ofusers. This relation suggests that users will benefit from direct net-work effects, as described in Section 2.2. The network effects mightNetwork effects

be dependent on the region, since a user’s utility of a high-definitionmap is only enhanced by those users whose movement patterns arelocated in the same region.

In 2015, the Open AutoDrive Forum (OADF) was founded to coordi-nate and align consortia on the topic of autonomous driving. Cur-OADF

rently multiple consortia are represented by OADF, each of whichconcerned with different aspects of maps and navigation [Nav17c].Table 3.9 lists the companies and organization, which are membersof the OADF and their consortia. The ADASIS Forum was establishedin 2001 aims at standardizing the interface between navigation sys-tems and advanced driving assistance systems. This includes the tra-

3.4 navigation and mapping 31

Table 3.9: Overview of consortia coordinated by the OADF.

Automotive

Organization Manufacturer Supplier Map data provider

Open AutoDriveForum (OADF) [Nav17c]

Audi, BMW, Daimler, Ford,Hyundai, Jaguar, LandRover, Mitsubishi, Opel

Aisin, Bosch, Denso,Panasonic

Baidu, Google,HERE, NavInfo,TomTom, Zenrin

Advanced DriverAssistance SystemsInterfaceSpecifications (ADASIS)[ADA17]

BMW, Daimler, Ford,Honda, Hyundai, Jaguar,Land Rover, Nissan, Opel,Renault, Toyota, Volkswa-gen

Aisin, Bosch,Continental, Denso,Magna, Panasonic,Valeo

Baidu, HERE,NavInfo, TomTom,Zenrin

Navigation DataStandard (NDS) [Nav17b]

BMW, Daimler, Hyundai,Nissan, Renault, Volkswa-gen

Aisin, Bosch, Denso,Panasonic

Baidu, HERE,NavInfo, TomTom,Zenrin

jectory of the road ahead, road gradients, traffic lights. The forum ADASIS

brings together vehicle manufacturers, navigation and assistance sys-tems provider and map data providers. It was originally launched byNavteq, a firm later bought by HERE, and was formally reorganizedto be coordinated by ERTICO. The forum is driven by the industryand open to public and private organizations [ADA17].

The second member of the OADF is the NDS Association, which pur-sues the target of standardizing map formats. Thus, it allows the mapproviders to store the data in their own proprietary format. Third par-ties can access the map due to the standardized NDS access format. NDS Association

The standard’s architecture is modular, as depicted in Figure 3.3, andsupports inter alia incremental updating [Nav17b].

High-definition mapping is estimated to create a $ 10–20 billionmarket by 2030 [Rol16, p. 10]. An early market entrance of map dataproviders might be essential, as additional users contribute data tothe offered real-time maps and therefore trigger network effects. Alist of relevant map data providers in various continents is given inTable A.5. Apart from Google, each map data provider is representedin all forums and associations. The vehicle manufacturer membersare predominately from Europe, USA, and Japan. This is similar forautomotive suppliers, whereas Aisin constitutes an exception. Vehiclemanufacturers can avoid strong dependencies on specific map dataproviders by promoting and adopting a standard.


Figure 3.3: Building blocks of the NDS specifications [Emd16].

4Q U A L I T Y S TA N D A R D I Z AT I O N D Y N A M I C S

A major issue of automated driving systems constitutes the assuranceof safety, since system failures can have lethal consequences. Thus,governments have a vital interest in regulating such systems in orderto protect their citizens. This can be achieved by setting the legisla-tive framework for manufacturers and mandating safety standards[Gas+12]. However, this additionally involves a technical challenge, Safety standards

as methods and procedures have to be developed to test highly auto-mated driving systems under a variety of conditions.

National and supranational legislation are also in competition toattract businesses and firms. Hereby, locational advantages can be re-alized by loosening the legislative restrictions. As large manufactur- Competition

ing firms and suppliers are operating in various markets throughoutthe world, investing and testing decisions are influenced by regula-tory frameworks. Clearly, standards ensuring properly functioningsystems belong to the category of minimum quality standards. Fur-thermore, the law enforcement of national and supranational legisla-tion could lead to geographically fragmented standards.

4.1 technical challenges

As software has been present in vehicles since 1976 and the amountof it is ever increasing, standards for software development processeshave already been developed [Pre+07, pp. 1 sq.]. A prominent ex- ISO 26262

ample constitutes the ISO 26262 standard, which defines functionalsafety requirements for electrical and electronic systems with respectto road vehicles [Hil12].

The V software development model, which is illustrated in Fig-ure 4.1, has long been applied in the field of automotive softwareengineering and is also referenced in the ISO 26262 standard. The leftside starts at specifying the requirements until the actual implemen-tation. Thereby, each of these steps is broken down into subsystems, V model

which are then treated in parallel. The right side validates and verifiesthe functionality of each level by testing [KW16, pp. 1 sq.].

Koopman and Wagner have identified multiple areas, in which thetesting of automated driving—level 4 of NHTSA—vehicles accordingto this model represents a major challenge. An obvious challenge arethe complex requirements. The V model assumes that the require-ments are known, complete, and specified in an unambiguous man-ner. Due to the abundance of different traffic scenarios combined withrare events, it seems rather impossible to compile a requirements doc-

34 quality standardization dynamics

REQUIREMENTSSPECIFICATION

SYSTEMSPECIFICATION

SUBSYSTEM/COMPONENT

SPECIFICATION

PROGRAMSPECIFICATION

MODULESPECIFICATION

SOURCECODE

UNIT TEST

PROGRAMTEST

SUBSYSTEM/COMPONENT

TEST

SYSTEMINTEGRATION

& TEST

ACCEPTANCETEST

VERIFICATION &TRACEABILITY

VALIDATION & TRACEABILITY




Review

Review

Review

Review

Review

Review

Review

Review

Review

Review

Review

Figure 4.1: Generic V model by Koopman and Wagner [KW16, p. 2].

ument, which handles all of these exceptions [KW16, p. 3]. Anotheridentified area is the challenge of realizing a safety concept from fail-safe to fail-operational [Mar+17, pp. 412 sq.]. Fail-safety describesChallenges

the capability of a system to bring itself into a fail-safe state to avoidhazards. In order to achieve fail-operational systems, three systemsrun in parallel and thus enable a redundancy if one of the systemsfails. Therefore, a fail-operational system remains operational, but de-graded if a subsystem fails [KW16]. The ISO 26262 working group iscurrently working on an enhanced version [Mar+17, p. 413].

4.2 germany

The German automotive industry takes the view that standardizedprocedures for experimenting and testing of automated driving sys-tems are required [PEG17]. Therefore, the Federal Ministry for Eco-nomic Affairs and Energy of Germany has initiated a project namedPEGASUS in 2016. The project’s mission is to create standardizedprocedures for testing and developing automated driving systems.Furthermore, this project aims at establishing quality criteria, meth-ods, and scenarios to allow for a rapid implementation of automateddriving and is structured into four sub-projects, which are shown inFigure 4.2.

Table 4.1 lists all involved partners in the PEGASUS project. Thereby,the project brings several stakeholders together, who are part of theautomated driving development process [PEG17]. Each German carmanufacturer and the two largest German suppliers are members ofthe project. Furthermore, the company TÜV SÜD, which is a test labproviding services to certify the conformance of standards, is also

4.3 european union 35

▪ How can complete-ness of relevant test runs be ensured?

▪ What do the criteria and measures for these test runs look like?

▪ What can be tested in labs or in simulation? What must be tested on test grounds, what must be tested on the road?

▪ Which tools, methods and processes are necessary?

▪What human capacity does the application require?

▪What about technical capacity?

▪ Is it sufficiently accepted?

▪Which criteria and measures can be deducted from it?

Scenario Analysis & Quality Measures

ImplementationProcess Testing

▪ Is the concept sustainable?

▪ How does the process of embedding work?

Reflection of Results & Embedding

Figure 4.2: Central issues of the PEGASUS project [Zlo+17, p. 4].

Table 4.1: Members of the PEGASUS project [PEG17].

Category Firms

Manufacturers Audi, BMW, Daimler, Opel, Volkswagen

Supplier Automotive Distance Control, Bosch, Continen-tal

Test Lab TÜV Süd

SMBs fka, iMAR, IPG, QTronic, TraceTronic, VIRES

Scientific institutes DLR, TU Darmstadt

involved in the project. Moreover, scientific research expertise is con-tributed by a university and a research institute.

Clearly, the development of such safety procedures comprises ini-tial upfront costs, whereas competencies of different fields are re-quired. A project initiated by the government increases the credi-bility, as the political framework plays a central role for automateddriving. Furthermore, the subsidies provide an incentive for partner-ing, whereas only German firms and German research institutes arerepresented in this project. The question arises whether a geographi-cally limited list of firms constitutes a beneficial factor. For example,the initial promoting group of the Bluetooth alliance comprised fivefirms with their headquarters in four different firms. However, the sit-uations are clearly different, since the development and adoption ofBluetooth was not highly dependent on the decisions of authorities.

4.3 european union

The EU has established several initiatives to facilitate advancements inthe transporting industry. The cooperation platform “ERTICO” wasfounded in 1991 and comprises over a hundred partners, whereasa selection is listed in Table 4.2. ERTICO’s partners include publicauthorities, research institutes, universities, and also users, such asassociations [ERT17]. Thereby, ERTICO targets at implementing and


Table 4.2: Partners of the European platform ERTICO and the consortium VRA.

Automotive

Organization Manufacturer Supplier Other

ERTICO[ERT17]

BMW, Fiat, Ford,Honda, Jaguar, MINI,Renault, Toyota,Volkswagen

Aisin, Bosch,Continental, Denso,Panasonic, Valeo

HERE, TomTom,Ericsson, Huawei,Deutsche Telekom,Telecom Italia,Vodafone

VRA [Veh17] Jaguar, Land Rover,Renault

Denso HERE

deploying enabling technologies and aims at evaluating related tech-nologies for ITS systems [ERT17]. ERTICO provides a platform toERTICO

coordinate the standardization requirements of the automotive indus-try. The compiled requirements are then passed onto SDOs, such as theSAE, IEEE, and ISO to facilitate a global harmonization [Eur14].

ERTICO coordinates inter alia the consortium Vehicle and RoadAutomation (VRA), which is funded by the European Commission.VRA’s mission is to identify deployment requirements for several ar-eas. This includes regulatory and legal aspects, as well as standard-VRA

ization and certification needs [Veh17]. Furthermore, the VRA consor-tium facilitates the cooperation between other EU projects and con-tributes to the trilateral cooperation between the EU, USA, and Japan,which is discussed in more detail in Section 4.4 [Arr16]. Despite VRA’spotential influence by contributing compiled requirements to the US

and Japan, only a minority of manufacturers and suppliers are mem-bers of the consortium.

4.4 eu-us-japan trilateral cooperation

International cooperation on information sharing regarding Intelli-gent Transportation Systems (ITS) has a long history. This exchangewas originated by the European Commission, the US Department ofTransport and the Road Bureau of Ministry of Land, Infrastructure,Transport and Tourism of Japan. The cooperation consists of a seriesMembers

of bilateral and trilateral agreements, whereas the exchange activitiesare officially authorized among each other [Eur17a, pp. 28 sq.].

Figure 4.3 illustrates the organization chart. Bilateral and trilateralworking groups have been established on different topics of interestand are coordinated and steered by superordinated groups [Eur17a,pp. 28 sq.]. The task groups cooperate on the harmonization of com-patibility as well as quality standards. Here, the working groupOrganization

4.4 eu-us-japan trilateral cooperation 37

Table 4.3: Selection of identified standardization requirements by VRA [Veh13, pp. 40-42].

Generic architecture Minimum performance and operational requirement stan-dardization of infrastructural elements. This also includequality standards from a single component level to the com-plete vehicle.

Cybersecurity Standardization regarding cybersecurity could reduce therisk of malicious attacks. For example, this includes firewallstandards and security certifications between communicat-ing elements.

Scenarios representing thereal world

Standardized minimum set of scenarios, which represent thereal world, minimize costs, and ensure vendor interoperabil-ity. Such standards are critical for development and valida-tion of automated driving systems.

Roadworthiness testing Testing standards not only address the need for develop-ment, but also for market deployment. Such standards in-clude system performance tests and fail-safe operation tests.

“Standards Harmonization” works on communication protocols andinfrastructure message standards.

Furthermore, a trilateral automation group was founded by theapproval of the steering committee in 2012. Their mission includesthe information exchange of the results obtained by regional researchprograms. The identification of standardization and harmonizationneeds in order to internationally develop and deploy automated driv-ing is also part of the work group’s mission. The involved parties haveagreed on eight areas, which are of shared interest, whereas only sixof them are depicted in Figure 4.3 [Eur17a, p. 29]. The area “road-worthiness testing” addresses the necessity of defining tests and testenvironments for the use on public roads. To achieve this goal, safetylevels and a methodology to validate such levels is researched in thisgroup. Moreover, this area also includes the topic on system valida-tion by means of standardized test environments [iMo13, p. 20].

The Chinese automobile market constitutes the largest automobilemarket in the world showing double-digit growth rates in recentyears and 28 million newly registered vehicles in 2016 [PwC16; Org17].In contrast, roughly 18 million vehicles have been sold in USA in 2016,whereas 17.5 million vehicles have been sold in the EU. Japan rep- Global effect

resents the fourth largest automobile market with 5 million vehiclesregistered annually [Org17]. Since the members of this trilateral coop-eration do not only account for the largest markets apart from China,but also provide a large proportion of the key vehicle manufactur-ers, a coordination on this level affects around 40 % of sold vehicles


Steering GroupCo-Chair: Takumi Yamamoto, MLIT/Ken Leonard, ITS-JPO US-DOT

/Colette Maloney, EC DG CONNECT

Coordinating GroupCo-Chair: Hideyuki Kanoshima, MLIT/Brian Cronin, ITS-JPO US-DOT

/Wolfgang Hoefs, EC DG CONNECT

Sustainability

Applications

Driver Distraction

& HMI

Standards Harmoniza

tion

Evaluation Tools and Methods

Safety Applicatio

ns

Government Only

AutomationProbe Data

Working Group

Trilateral WG

：Trilateral WG

：EU-US WG

US-Japan（Japan:observer）

：US-Japan WG

Harmonization Task Group

HTG1 & HTG3ITS Security and Communications

Protocols

HTG2Safety Message

Harmonization ‐US BSM and EU CAM

HTG4/5Infrastructure Message

Standards

HTG6ITS Security Policy for

Cooperative ITS Environments

HTG7(Candidate New Work Item) Gap & Overlap

Analysis

HTG8(Candidate New Work

Item) Probe Data Standards

Trilateral WG

Sub-Group

Accessible Transport

Evaluation Roadworthiness testing and certification

Human Factor Digital Infrastructure

Legal Issue

Figure 4.3: Structure of the trilateral cooperation as of 2015 [Zlo+17, p. 4].

worldwide. Therefore, safety standards authorized and enforced bythese markets clearly have the potential to attract followers.

Part II

P E D E S T R I A N B E H AV I O R M O D E L

5L I T E R AT U R E R E V I E W

Over the last decades several models have been developed to simulatethe dynamics of pedestrian behavior. Thereby, different modellingtechniques have been applied, which are reviewed in the following.Moreover, models specifically proposed for intersection situations arediscussed.

5.1 scales of modelling

Pedestrian behavior models can be differentiated according to theirspatial resolution. Figure 5.1 illustrates three different scales of mod-elling pedestrian behavior. The macroscopic approach is based on

Figure 5.1: Macroscopic (left), mesoscopic (middle), and microscopic (right) modelling approach.

aggregating pedestrians to traffic flows, rather than modelling sin-gle pedestrians. Modelling traffic flows is often achieved by means Macroscopic

approachof fluid or continuum mechanics theories [Pap+09, p. 243]. Since theobjective of this thesis is to model pedestrians interacting with cars,macroscopic models are not further considered and discussed.

Mesoscopic models describe pedestrians as individuals, which arenot able to move continuously in space. The movement is restrictedto discretized positions on a two-dimensional grid, as shown in Fig-ure 5.1. This type of modelling has the advantage of increased com- Mesoscopic approach

putational efficiency, due to the limited number of cells and states onthe grid. However, the discretization of space introduces an accuracyproblem for cells, which are partially blocked by an obstacle. Allow-ing pedestrians to move onto partially blocked cells leads to pedes-trians moving into obstacles. On the contrary, if pedestrians do notmove onto such cells, their moving space is more limited comparedto the reality [Bie+16, p. 352].

42 literature review

Microscopic models, depicted on the right of Figure 5.1, describepedestrians as individuals, which are able to continuously move inspace. This type of modelling enables pedestrian movements withMicroscopic

approach high spatial resolution and thus allows for more detailed simulationresults. However, the increased resolution comes at the cost of highercomputational complexity [Bie+16, pp. 352 sq.].

Simulating pedestrian behavior does not necessarily mean that thedeployed model follows one of these approaches. Techniques to cou-ple models of different scales have been proposed by Biedermann etal. [Bie+14; Bie+16].

5.2 pedestrian behavioral levels

Hoogendoorn et al. proposed a concept to distinguish pedestrianbehavior into three levels, which are listed in Table 5.1 [Hoo+01;HB04]. Each of those levels exchanges information with the level

Table 5.1: Pedestrian behavioral levels according to Hoogendoorn and Bovy [Hoo+01; HB04].

Strategic level On the strategic level pedestrians choose what activities to performand thus identify a destination.

Tactical level In order to reach the identified destination, a route has to beplanned on the tactical level.

Operational level Finally, the actual movement of the pedestrian along the plannedroute takes place on the operational level.

above and/or below [HB04]. This behavioral categorization has foundwide application in research [Pap+09, p. 252; Rob+09, p. 37].

5.3 related models

Some developed models describe situations at intersections, whereasothers also model the interaction between pedestrians and cars. Aselection of models which are related to the objective of this thesisare discussed in the following.

5.3.1 Feng et al. 2013

In order to simulate the crossing of pedestrians at signalized streets,Feng et al. based their model on a cellular automata, as depicted inFigure 5.2. Thereby, the space of the crossing is divided into cellswith a fixed width of 0.6 m, which approximately corresponds to apedestrian possibly carrying a bag. The pedestrians are divided intotwo categories: up-walkers and down-walkers. The movement intoan adjacent cell is modelled with dynamic transition probabilities,

5.3 related models 43

which are influenced by three parameters. First, the benefit parame-ter describes the pedestrians’ general direction to reach the destina-tion, which is achieved by increasing the transition probabilities intothe destination’s direction. Second, the attraction parameter modelsthe attraction to other pedestrians who move into the same direction.Third, the occupancy parameter restricts the movement to cells occu-

Figure 5.2: Pedes-trians on a squarelattice.

pied by another pedestrian [Fen+13].Since the pedestrians’ positions are discrete, this model falls under

the category of mesoscopic models. This in turn introduces the limi-tations of the mesoscopic approach, as discussed in Section 5.1. Fur-thermore, Feng et al. do not take cars or other vehicles into account,which could partially block cells on the crossing space.

5.3.2 Hashimoto et al. 2016

Hashimoto et al. developed a probabilistic behavior model for signal-ized crossing situations, which is based on a Dynamic Bayesian Net-work (DBN). Figure 5.3 depicts the directed acyclic graph of the DBN.Each node represents a random variable, which can have discrete or

Signal

Motion

Speed

Position

Observation

Direction

Zt−1

Pt−1

Spt−1

Drt−1

Mt−1

Dt−1

St−1

Zt

Pt

Spt

Drt

Mt

Dt

StDecision

Figure 5.3: DBN of the pedestrian behavior model by Hashimoto et al. El-lipses denote continuous random variables and rectangles repre-sent discrete random variables [Has+16, p. 169].

continuous values. The edges of the graph represent the conditionaldependencies between the nodes.

Hashimoto et al. introduced several random variables, starting withthe traffic signal St ∈ {PG, PFG, PR}. These values represent thethree phases of Japanese traffic signals. St obviously influences the Random variables

pedestrian’s decision Dt ∈ {cross, wait}. Mt models the different mo-tion types, such as standing, walking, and running. Furthermore, Spt

denotes the pedestrian moving speed; Drt the pedestrian’s movingdirection and Pt the pedestrian’s position. The random variable Zt de-scribes the measured position of the pedestrian [Has+16, pp. 168 sq.].

This behavior model is particularly targeted for vehicles at sig-nalized intersections to assess the risk of a possible collision with


a pedestrian. The different behavioral levels are closely linked toeach other, as they are part of a single DBN. Furthermore, this typeof model requires a dataset to estimate the conditional probabilities.Introducing a new contextual random variable, such as the overalltraffic volume at the crossing, requires the contextual information tobe included in the dataset. For example, less traffic might increase theprobability of pedestrians crossing during red signal phases.

5.3.3 Anvari et al. 2015

Anvari et al. developed a behavior model for vehicle-pedestrian inter-actions in shared space environments. Shared spaces describe streetsor places, which are used by pedestrians and vehicles together. Suchspaces are designed by reducing the distinction between pedestrianareas and streets [Anv+15, p. 85].

Anvari et al.’s shared space model describes not only the behaviorof pedestrians, but also for cars. The model is structured into threelayers, whereas the first is responsible for the trajectory and the plan-ning. In order to reach the final destination, intermediate destinationsare identified by a flood-fill algorithm. Furthermore, the trajectoriesStrategic and

tactical behaviorlevel

to the different destinations are planned by using the shortest path[Anv+15]. Thus, this layer undertakes the tasks of the tactical andstrategical behavioral level according to Hoogendoorn et al.

A modified version of Helbing and Molnár’s social force model[HM95] is utilized for Anvari et al.’s second layer [Anv+15]. Thereby,the pedestrian’s operational behavior is modelled as sum of severalforces, which affect the pedestrian. Anvari et al. argues that pedes-Operational behavior

level trians and cars move with identical priorities in shared spaces andtherefore the social force approach is also applied to cars, as shownin Figure 5.5 [Anv+15, p. 91]. Compared to the original model of Hel-

#„

Fγ1

#„

Fγ2#„

Fγ3#„

Fγn

#„

Fα1#„

Fα2

#„

Fα3

#„

Fαn

Figure 5.4: Modified social force model for shared spaces by Anvari et al.

bing and Molnár [HM95], Anvari et al. has introduced forces to modelthe influence of pedestrians on cars and vice versa. For example, a re-

5.3 related models 45

pulsive force exerted by cars on pedestrians is added to describe thecircumstance that pedestrians avoid potential collisions.

The third layer defines several rules, such as constraints for steer-ing angles and movement speeds for cars. Maximum steering angles,no lateral movements, speed limits, and speed reductions for curvedtrajectories are modelled by means of constraints. Furthermore, rules Rule-based

constraintsfor conflict avoidance are introduced to describe car-pedestrian andcar-car interactions. This includes a left hand driving preference, forshared spaces in the UK [Anv+15, pp. 98 sq.].

5.3.4 Zeng et al. 2014, 2017

Zeng et al. have presented models to describe pedestrian behaviorat signalized crossings [Zen+14b; Zen+17]. Each of these microscopicmodels is based on the concept of social forces and differs in thescope of modelling. The model, shown in Figure 5.5, was published

N1

Waiting area

Desired exitposition ~Pexit

N2

F2F1

Desired enteringposition

1

2

3

Figure 5.5: Signalized crosswalk of Zeng et al.’s model.

in 2014 and considers only a single signalized crosswalk [Zen+14b].It consists of four Origin Destination (OD) zones (F1, F2, N1, N2) andis structured into three stages, which are depicted as circled numbers. Strategic behavior

levelIn order to model entering and exiting positions, Zeng et al. con-ducted an empirical analysis to estimate the position distributions.The identification of the desired entering and exiting positions is partof the strategic behavioral level. The go decision after a green signalphase is modelled as a Weibull distribution. However, the main focusis on the second stage, the actual road crossing [Zen+14b, p. 145].

The operational level of Zeng et al.’s model is based on the so-cial force approach, whereas additional forces were introduced. Thisincludes an attractive force directed towards the middle of the cross-walk and a repulsive force from cars.

Zeng et al.’s model, which was published in 2017, describes pedes-trian behavior at a complete signalized intersection, rather than a sin-gle crosswalk. Therefore, the OD zones are placed at the beginnings


and endings of the sideways. In order to route towards the destina-tion, a finely-meshed cell-based network is introduced, which servesas the basis for a navigation graph. Each cell comprises a node and isinterlinked to the adjacent cells, whereas the edge costs correspondto the Euclidean distance. A shortest path algorithm is applied forthe tactical level, in which cells can be blocked by other pedestrians.However, the model does not address pedestrians, who intentionallytake a shortcut, since the navigation graph is only available for side-walks and crosswalks. The operational level is similar to the modelpublished in 2014 [Zen+17].

5.3.5 Overview of Models

Table 5.2 provides an overview of the previously discussed models.

Table 5.2: Overview of pedestrian behavior models.

Authors Scale Approach Situation

Feng et al. [Fen+13] Mesoscopic Cellular automata Pedestrians crossing the street

Hashimoto et al. [Has+16] Microscopic Bayesian Network Signalized intersections

Anvari et al. [Anv+15] Microscopic Pedestrians: Social force,Cars: Rule-based social force

Shared space environments

Zeng et al. [Zen+14b] Microscopic Social force Signalized crosswalk

Zeng et al. [Zen+17] Microscopic Social force Signalized intersection

6P E D E S T R I A N B E H AV I O U R M O D E L

In order to simulate the behavior of pedestrians, a theoretical modelis required. In the following, the theory of the developed model isdescribed, whereas the chapter is structured into the three behav-ioral levels introduced by Hoogendoorn et al. [Hoo+01; HB04], asdiscussed in Section 5.2.

6.1 strategic level

According to Hoogendoorn et al. [Hoo+01], pedestrians choose theirnext activities and identify a destination on the strategic level. It isassumed that the modelled pedestrians want to reach a destinationoutside of the intersection area. This could include a shop, their home,and the railway station. Since the scope of the model is limited to theintersection area itself, pedestrians will only traverse the intersection. Destination

identificationIt is assumed, that no intermediary goals, such as stores or benches,are present in the intersection scenario.

As depicted in Figure 6.1, origin and destination areas are placed atthe beginnings and endings of sidewalks. A Markov chain approach

Destination

OriginSidewalk

Crosswalk

Footpath

Figure 6.1: Exemplary intersection with origin and destination areas.

is utilized to model the destination selection of pedestrians. Thereby,each origin and destination is represented as node in a graph andthe selection of a destination is described by transition probabilities.The probabilities can be expressed in a transition matrix, which is

48 pedestrian behaviour model

commonly referred to as origin-destination matrix in pedestrian dy-namics research [BR11].

6.2 tactical level

Provided the case that a destination was identified on the strategicbehavioral level, the route choice to reach the destination is modelledon the behavioral tactical level [HB04, p. 172].

Pedestrians have dedicated areas to reach their destination at anintersection. As shown in Figure 6.1, the dedicated areas include side-walks, crosswalks, and footpaths. In an ordinary situation, pedestri-ans generally prefer these dedicated areas to navigate towards theirdestination. However, there also exist situations in which a pedestrianwill cross the road without taking a detour via a crosswalk. It is as-sumed, that pedestrians have different risk tolerances and that theyperceive a situation differently. Therefore, one pedestrian might tol-erate the risk of diagonally crossing an intersection, whereas anotherone avoids this risk. This may be due to the preference of saving timeSituation evaluation

or due to a higher individual risk tolerance.Figure 6.2 shows a crosswalk overlaid by an undirected navigation

graph in orange. A graph consists of nodes, which are referred to asnk, and edges denoted as ek↔l . Furthermore, #„nk denotes the positionof nk and d(ek↔l) describes the Euclidean distance of ek↔l . In thisexemplary situation, a pedestrian originates in the green area andidentifies the blue area as destination. Subsequently, the pedestrian

n1 n2 n3

n5n4

e1,5 e3,5

Figure 6.2: Exemplary pedestrian crossing with navigation graph in orange.The green area denotes the origin of a pedestrian and the bluearea illustrates the destination.

has to navigate through the situation to reach the destination. The setA denotes the different types of areas, so that

{crosswalk, sidewalk, street, . . . } ∈ A. (6.1)

In order to model the path finding, each edge ek↔l has a cost ck↔l ,which takes the risks of the area a ∈ A into account. Therefore, thevariable da(ek↔l) denotes the length of ek↔l , which is placed on area

6.3 operational level 49

a. The cost is then modelled by the following formula, whereas pi,adenotes the perceived cost function of pedestrian i regarding area a:

ci,k↔l = ∑∀a∈A

pi,a(da(ek↔l)) (6.2)

A navigation solely based on the distance would be realized by theperceived cost function pi,a(da) = da, ∀a. It is assumed, that edge sec-tions, which are located on areas dedicated to pedestrians, constitutea lower perceived cost. This is due to a lower exposure to risk, whichincludes the risk of a car accident, for example. Subsequently, pedes-trian i asses the cost of taking the edge ek↔l by the following formula:

pi,a(da) =

dasi, if a = crosswalk, sidewalk, and footpath

da, otherwise(6.3)

Here, the parameter si ∈ [0, 1] denotes the strength of preferring areas,which are dedicated for pedestrians. If s1 = 0, pedestrian 1 will takeany detour, to avoid the risk of walking on an area, which is notdedicated for pedestrian purposes. It is assumed that si is normallydistributed, so that si ∼ N (µs, σ2

s ).

6.3 operational level

As discussed in Section 5.2, the operational level addresses the phys-ical movement along the planned route.

To describe the operational behavior, a set of variables is intro-duced. The position of pedestrian i is denoted as #„xp,i and the ve-locity is indicated as #„vp,i. Further, the desired velocity of pedestriani is indicated as vd

p,i, whereas the next target is denoted as #„x dp,i. Sim- Variables

ilarly, the car k is also described by position and velocity variables,but with the index c, k. Since the space is modelled as planar, eachvector #„xp,i,

#„vp,i,#„xc,k, #„vc,k ∈ R2×1 is in the two-dimensional Euclidean

space. The variables vp,i = ‖ #„vp,i‖ and the desired velocity vdp,i ∈ R

are scalars.The time is discretized and thus, described as tk = k∆t, whereas k

denotes the simulation step and ∆t the duration of a time step. Thevelocity of pedestrian i at time tk can be obtained by:

#„vp,i(tk) =#„vp,i(tk−1) +

#„ap,i(tk)∆t (6.4)

Here, #„ap,i denotes the acceleration of pedestrian i. Subsequently, theposition of simulation step k can be computed by means of the fol-lowing formula:

#„xp,i(tk) =#„xp,i(tk−1) +

#„vp,i(tk)∆t (6.5)

The velocity change of pedestrian i is then modelled by means of so-cial forces. These social forces describe the pedestrian’s motivation


to act and are influenced by environmental effects, such as otherpedestrians or obstacles. However, social forces do not describe forces,which are exerted on the body of pedestrians [HM95, p. 51]. Theforces can stem from different effects and are modelled to follow thesuperposition principle. The resulting force of the model is obtainedby the following formula:

#„

Fp,i(tk) = mp,i#„ap,i(tk) =

#„

F drip,i +

#„

F pedp,i +

#„

F carp,i +

#„

F crop,i (6.6)

Here, mp,i denotes the mass of pedestrian i. However, multiple modelsdo not explicitly include the pedestrian’s mass and therefore, simplifyit by assuming it to be 1 kg [Anv+15, p. 89; Zen+14b, p. 146; Zen+17,p. 42]. Clearly, including or excluding the modelling of pedestrianmasses has an impact on several parameters.

6.3.1 Driving Force

In order to reach the next target, the pedestrian will steer towards it.Therefore, the desired direction of pedestrian i can be expressed as:

#„

d dp,i =

#„x dp,i −

#„xp,i

‖ #„x dp,i −

#„xp,i‖(6.7)

Helbing and Molnár introduced the driving force, which attracts thepedestrian towards the next target [HM95]. In the case of no otherdisturbing influences, the pedestrian will move in the desired direc-tion and will approach the desired velocity with the relaxation timeτp. The driving force is modelled by:

#„

F drip,i =

1τp

(vd

p,i#„

d dp,i −

#„vp,i

)(6.8)

6.3.2 Conflicting Pedestrian

A pedestrian influences the motion of other pedestrians, due to desir-ing a private sphere and avoiding collisions [HM95, p. 51]. Zeng et al.assume that the movement of other pedestrians creates an ellipticalforce field. Further, it is assumed that this force is only exerted ona pedestrian if the conflicting pedestrian is in visual range [Zen+17,p. 43].

#„vp,i

#„vp,j

#„ci↔j

Figure 6.3: Pedes-trians i and j poten-tially colliding atpoint #„ci↔j.

The visual perception of pedestrians is modelled as a cone directedtowards the heading of a pedestrian. The sight cone is described byan opening angle γv and a distance rv, which represents the visualrange and is depicted in Figure 6.4 [Zen+17, pp. 43 sq.]. Furthermore,a situation in which pedestrian i perceives pedestrian j is illustratedin Figure 6.3. If both keep their current velocities constant, a collisionwill occur at point #„ci↔j.


The time to conflict describes the duration until pedestrian i reachesthe collision point #„ci↔j and is computed by: Time to conflict

TTCi,i↔j =‖ #„ci↔j − #„xp,i‖‖ #„vp,i‖

(6.9)

Subsequently, TTCj,i↔j describes the duration until pedestrian j ar-rives at the collision point [Zen+17, pp. 43 sq.]. Furthermore, the rel-ative time to conflict measures the timing difference between the ar-rival of pedestrian i and j. The relative time to conflict is given by: Relative time to

conflict

RTTCi↔j =

∣∣TTCi,i↔j − TTCj,i↔j

∣∣ , if collision point exists

+∞, otherwise(6.10)

The modelling of the force field is shown in Figure 6.4, whereas theforce

#„

f pedj→i is exerted from pedestrian j onto pedestrian i. The nor-

#„vp,i

#„xp,j

#„vp,j ∆t

#„

bj→i r v

γv

#„

f pedj→i

Figure 6.4: Repulsive force from conflicting pedestrian j exerted onto pedes-trian i. The ivory cone illustrates the vision of pedestrian i withopening angle γv and vision range rv.

mal vector #„nj→i describes the perpendicular to the ellipse at the posi-

tion #„xp,i of pedestrian i. As depicted in Figure 6.4, the force#„

f pedj→i =

f pedj→i

#„nj→i is perpendicular to the ellipse [Zen+17, p. 44]. Thereby, thesemi-minor axis of the force field is described by the following for-mula:

bj→i =12

√(‖ #„

dj→i‖+ ‖#„

dj→i − #„vj∆t‖)2− ‖ #„vj∆t‖2

#„

dj→i =#„xp,i − #„xp,j.

(6.11)


The force#„

f pedj→i is described by the following formulas:

#„

f pedj→i = Ar

β exp(−Brβbj→i − BβαRTTCi↔j)ω(ϕi,j)

#„nj→i (6.12)

#„

F pedp,i =

np

∑j=1j 6=i

#„

f pedj→i (6.13)

The force#„

F pedp,i expresses the superposition of np individual conflict-

ing pedestrians and the parameters Arβ, Br

β, Bβα have to be estimatedby calibrating the model.

The angular dependence of pedestrian interactions was proposedby Johansson et al. [Joh+07] and the concept was incorporated intoZeng et al.’s operational model [Zen+17]. It describes the effect thatthe interaction is assumed to be stronger, if a pedestrian is directly infront compared to being on the side. Therefore, let ϕi,j be the anglebetween #„vp,i and

#„

di→j, as depicted in Figure 6.5. The variable ω(ϕi,j)#„vp,i

#„

di,j

ϕi,j

Figure 6.5: Angleϕi,j between pedes-trian i and j.

describes the angular effect in Equation 6.12 and is calculated by:

ω(ϕi,j) = q(ϕi,j)

(λα + (1− λα)

1 + cos(ϕi,j)

2

)(6.14)

The parameter λα ∈ [0, 1] denotes the strength of the effect [Zen+17,p. 44]. The function q(ϕi,j) controls, that the effect only occurs whenthe pedestrian is in front and is denoted as:

q(ϕi,j) =

1, if |ϕi,j| ≤ π/2

0, otherwise(6.15)

However, an opening angle of γv ≤ π leads to |ϕi,j| ≤ π/2, since onlypedestrians located in the visual cone are considered anyway.

6.3.3 Conflicting Car

If a car k is in the pedestrian’s visual range, a repulsive social forcefrom the car is generated. Such a situation with two pedestrians i andj is shown in Figure 6.6.

The force from k onto pedestrian i is formulated by:

#„

f cark→i =

Ac exp(−Bc‖ #„xp,i −#„

bp,i‖) #„nk→i, if #„vp,i · #„ni→k > 0

0, otherwise(6.16)

Here,#„

bp,i is the closest point on the car’s boundary to pedestriani. Subsequently, the vector #„nk→i describes the normalized directionfrom

#„

bp,i to the #„xp,i. The model parameters Ac, Bc denote the interac-tion strengths and have to be estimated [Zen+17, p. 48].


#„xp,i

#„vc,k

#„

f cark→i

#„

f cark→j

Figure 6.6: Car k exerting repulsive forces exerted on pedestrian i and j.

In the case of multiple cars, it is assumed that the interaction forcesfollow the superposition principle:

#„

F carp,i =

np

∑k=1

#„

f cark→i (6.17)

6.3.4 Crosswalk Boundary

Zeng et al. observed an effect that pedestrians tend to walk inside theboundary of crosswalks [Zen+14b, p. 146]. This situation is depictedin Figure 6.7. The reaction of pedestrian i to a crosswalk is mathemat-

#„

bp,j#„

F crosp,i

#„

F crosp,j

#„

bp,i

#„xp,j

#„xp,i

Figure 6.7: Social forces of pedestrian i and j on the crosswalk. The bluelines denote the boundaries of the crosswalk.

ically formulated by:

#„

F crosp,i =

Arb exp(−Br

b‖#„xp,i −

#„

bp,i‖) #„nb→i, if inside crosswalk

Aab exp(−Ba

b‖#„xp,i −

#„

bp,i‖) #„ni→b, if outside crosswalk

(6.18)

In this case,#„

bp,i denotes the closest point on the crosswalk’s bound-ary to pedestrian i. If a pedestrian is outside the crosswalk, a force to-wards the crosswalk will attract the pedestrian towards the boundary#„ni→b. The model parameters Ar

b, Brb, Aa

b, Bab describe the strengths of

the forces and have to be determined by calibration [Zen+17, pp. 48 sq.].

7I M P L E M E N TAT I O N

The simulation of the pedestrians and cars is performed in a dis-tributed manner. In the following, the utilized simulation softwareand the communication software is explained. Moreover, the imple-mentation of the model and the further development of the commu-nication software are discussed.

7.1 simulation setup

The simulation consists of two simulating processes and an intermedi-ary process, whereas the setup is schematically shown in Figure 7.1.The software Virtual Test Drive (VTD) is a driving vehicle simulator

Virtual Test Drive MomenTUMv2Intermediary ProcessCars

Pedestrians

Cars

Pedestrians

SCP, RDB ActiveMQ

Figure 7.1: Setup of the distributed simulation.

and is discussed in more detail in Section 7.2. Then, the informationof the simulated cars is sent via the Runtime Data Bus (RDB) proto-col to the intermediary process. This includes information about the VTD

cars, such as positions, velocities, and directions.The task of the intermediary process is to coordinate the simula-

tion procedure. Initialization, starting and stopping the simulationrequires coordination of the involved simulators. Moreover, the sim- Intermediary process

ulators have to be synchronized during runtime.The second simulation software is MomenTUMv2, which is a pedes-

trian simulation framework. The pedestrian model, which was dis-cussed in Chapter 6, was implemented by utilizing the provided func-tionality of the framework. During runtime, the framework receives MomenTUMv2

current information of the cars from the intermediary process andsends the information of the simulated pedestrians to the intermedi-ary.

The concept of the simulation setup was part of a previous thesis[Sch17, pp. 5 sqq.]. However, the functionality of the intermediaryprocess was further developed during the course of this thesis. This

56 implementation

particularly includes the exchange of car information from VTD to-wards MomenTUMv2.

7.2 virtual test drive

The software “VTD” is a tool-chain for simulating driving applicationsin the automotive sector. In this sector, VTD is used for developing andtesting driver assistance systems with automated driving functional-ity [VIR17b]. The tool-chain supports several steps in the simulationprocedure. VTD comprises a builder to create road networks and 3Dgraphics, which is shown on the left of Figure 7.2. The created roadnetworks can be exported as an open format “OpenDRIVE”.

Figure 7.2: Creation of the road network (left) and setting the scenario (right) with VTD.

The image on the right shows an editor, where the scenario can beset up. Cars can be placed on the previously designed map and theirattributes can be manipulated by means of this editor. Furthermore,Editor

triggers can be set to start lane changes, speed changes, and receiveSimulation Control Protocol (SCP) signals for example [VIR17b].

SCP and RDB refer to the protocols, which enable the communica-tion between VTD and other elements. Thereby, VTD is used to supportvarious steps in the automotive engineering process. Software in-the-Protocols

loop describes the testing of software models within a simulation,whereas hardware in-the-loop means the testing of hardware compo-nents within a simulation. Testing software models in combinationwith a driver is subsequently named driver in-the-loop testing. VTD

supports the environment simulation for each type of testing. Clearly,to test a component by means of simulating the environment, the in-terface format has to cover the objects, which are perceived by othercomponents [VIR14].

7.3 intermediary process 57

7.3 intermediary process

The intermediary process was originally implemented as part of athesis about developing a proof-of-concept. The concept showed thatMomenTUMv2 could contribute simulated pedestrians to traffic sce-narios in VTD [Sch17]. Therefore, the first step was to implementthe sending of pedestrian information from MomenTUMv2 to VTD.This was achieved by utilizing the tool “Automotive Data and Time-Triggered Framework (ADTF)”. It is provided with a toolbox, which Implementation

manages the communication to VTD via the protocols SCP and RDB.Since the behavioral model of pedestrians is dependent on the vehi-cles, the simulated cars have to be sent from VTD to MomenTUMv2.

To realize the bidirectional exchange of simulated objects, the net-work protocol was adjusted and enhanced, as shown in Table 7.1. TheADTF-toolbox allows to obtain prepared information from VTD’s net-work protocols by inheriting callback functions. Thus, an up-to-date Enhanced protocol

list of cars with attributes is stored at the intermediary process. How-ever, the interface to MomenTUMv2 requires a protocol, which sup-ports the structured information exchange of pedestrians, cars, andpotentially other dynamic objects, such as traffic lights.

Table 7.1: Serialization of simulated objects for network transmission.

JavaScript Object Notation (JSON) representation

Pedestrians Cars Type Unit

[{ "id" : "1", [{ "id" : "1", integer

"type" : "pedestrian", "type" : "car", string

"timeStep" : "5", integer

"time" : "2.0", "time" : "2.0", double s

"x":"10.7", "x":"5.31", double m

"y":"8.1", "y":"8.03", double m

"xHeading":"0.65", "xHeading":"1.0", double m

"yHeading":"0.76", "yHeading":"0.0", double m

"xVelocity":"0.3", "xVelocity":"5.4", double m s−1

"yVelocity":"0.2", "yVelocity":"0.0", double m s−1

"bodyRadius":".23"}, "sizeLength":"4.9", double m

"sizeWidth":"1.9", double m

"sizeHeight":"1.4"}, double m

{ "id" : "2", { "id" : "1", integer

"type" : "pedestrian", "type" : "car", string

"timeStep" : "5", integer

"time" : "2.0", "time" : "2.0", double s

"x":"5.31", "x":"5.31", double m

"y":"8.03", "y":"8.03", double m

...}] ...}]

58 implementation

7.4 momentumv2

In order to simulate pedestrians in various environments, theorieswhich describe the behavior of pedestrians are required. Many dif-ferent theories and models describing pedestrian behavior have beenproposed and developed. However, the various theories can be clas-sified into different categories. This includes the categorization intostrategic, tactical and operational behavior levels, which was intro-duced by Hoogendoorn et al. [Hoo+01] and was discussed in Sec-tion 5.2.

To implement and simulate different behavior models Kielar et al.developed the modular and generic framework MomenTUMv2 forpedestrian dynamics research [Kie+16]. The framework facilitates theimplementation of new models by providing an already existing setof functionality, such as models to generate or absorb pedestrians.Moreover, MomenTUMv2 comprises visualization tools, map genera-tion tools, and methods for analyzing the simulation results. Thereby,MomenTUMv2

framework MomenTUMv2 follows a modular approach by specifiying informa-tion interfaces between the models. This enables the exchange of sin-gle models and therefore facilitates the comparison between imple-mented behavior models [Kie+16, pp. 10 sq.].

The framework is based on an agent-based approach to simulatemultiple pedestrians. Thus, pedestrians are simulated as individualentities. Figure 7.3 illustrates the structure of MomenTUMv2, whereasthe behavior models are depicted in gray. The framework followsHoogendoorn et al. concept by differing between strategic, tactical,and operational models [Kie+16, p. 12]. MomenTUMv2 is implementedin Java and comprises multiple packages, which are listed in Table 7.2

Table 7.2: Overview of the MomenTUMv2 project [11 sqq. Kie+16].

Package Description

Configuration This packages manages the configuring of all classes by processing the XML file.

Data The data packages includes classes and interfaces representing inter alia pedestrians,their states, and different areas.

Infrastructure This package provides mechanisms which are essential for the simulationprocedure, such as the time manager, and exception handling.

Model This package contains the all models structured by model type.

Simulator Manages and coordinates the simulation procedure.

Third Party This folder contains third-party libraries.

Tools This package includes layout tools, such as the AutoCAD plugin for exporting.

Utility Supports the other packages by providing mathematical algorithms, such as graphalgorithms, and geometrical computations.

Visualization Contains Graphical User Interface (GUI) for visualization.

7.4

mo

me

nt

um

v2

59

Process Generators

· Pedestrian generators· Pedestrian seeds· Car generators

Tactical Models

· Routing models· Queuing models· Searching models· Participating models Operational Models

· Walking models· Standing models

Start

Termination

Strategic

· destination choice models· standing models

Strategic Models

· Destination choice models

Process Analyses

· Time-based analysis· Unit-based analysis

Process Absorbers

· Pedestrian absobers

Support Models

· Query models· Perception models

Additional Models

· Meta modelsPre-Processings

· All models· Network

ConfigurationProcessings

Behavior Models

Process Writers

· Data output writers· Model output writers· Network output writer

Post-Processings

· All models· Network

CycleHandling

LoopHandling

Process Models

UpdateHandling

ThreadHandling

OrderHandling

Config.xml

· Layout· Configurations· Network

Process Network

· Control messages· Handle waiting

Figure 7.3: Structure of the framework MomenTUMv2 developed by Kielar et al. [Kie+16, p. 9] and extended by a network interface, depicted in green[Sch17, p. 21].

60 implementation

To implement the models, discussed in Section 6.2 and Section 6.3,several functionalities were added to multiple packages.

7.4.1 Car Manager

The information of the cars is received over the network interface. Tofurther utilize this information and access the cars in the models, twosteps are carried out. First, the information is deserialized and pro-cessed. This is performed by means of a generator in the model pack-Deserialization and

data processing age, which connects to the network and then receives the simulationmessages in JSON format during each simulation cycle. Furthermore,a CSV file import was implemented to rerun pedestrian simulationswith the same traffic.

Second, for storing and accessing the cars, a car manager class wasadded to the data package. Further, static and operational states andInformation

management the class representing the car were implemented.

7.4.2 Additional Areas

As it was discussed in Chapter 6, the tactical model requires infor-mation about areas, which are dedicated for pedestrian use, such ascrosswalks and sidewalks. Furthermore, the force

#„

F crosp,i of the op-

erational model requires information about the boundaries of thecrosswalks. Figure 7.4 shows the drawing of a scenario by means

Figure 7.4: AutoCAD with overview of layers and layer groups.

of the CAD-tool AutoCAD [Aut17a]. The created scenario can thenbe exported to an XML file for MomenTUMv2 by utilizing a pluginfor AutoCAD, which is part of the tool package [Kie+16]. The plu-

7.4 momentumv2 61

gin was expanded to also export polygons representing crosswalksor sidewalks. In order to export polygons placed on the layers “Cross-walk” or “Sidewalk”, the layers have to be added to the layer group“TaggedAreas”. Subsequently, the plugin will export the polygons onthe layer “Crosswalk” as Extensible Markup Language (XML) nodes“TaggedArea” with type “Crosswalk”.

7.4.3 Geometry

Since several geometrical operations are performed by the tactical andoperational model, functionality was added to the utility package.

As discussed in Section 6.3.2, a pedestrian influences other pedestri-ans by walking in their visual range. In order to compute social forcesfrom potential collisions with other pedestrians, it is necessary to findthe conflict point. Therefore, the movement of each pedestrian can begeometrically represented by the means of a ray. A ray comprises an

#„x

#„

dinitial point #„x representing the position of a pedestrian and a direc-tion

#„

d representing the velocity vector. As depicted in Figure 7.5, tworays can have different relations to each other. The geometrical op-

a) b) c) d)

Figure 7.5: Possible intersections of two rays.

erations utilized in MomenTUMv2 are mostly based on the collisiondetection library dyn4j [Bit17]. Since the calculation of intersectionsbetween rays was not part of dyn4j’s ray class, this functionality wasadded to MomenTUMv2’s wrapper class.

Once the potential collision point is determined, the magnitude ofthe force ‖ #„

f pedj→i ‖ is calculable, as discussed in Section 6.3.2. However,

the direction of this force is perpendicular to the elliptic force field.As illustrated in Figure 7.6, the normal vector #„n at point #„x , whichrepresents the pedestrian, has to be obtained. Therefore, an ellipse Ellipses

class was added to the utility package, which is based on the Geo-Regression library [Abe17]. This library comprises the functionalityof algorithmtically estimating the closest point #„x on the ellipse toanother point #„y . This enables the obtainment of normal vectors forpoints that are either placed on the ellipse or not.

The car is geometrically represented as a rectangle and thus, a class,which wraps the rectangle class of the dyn4j library, was introduced

62 implementation

#„

f 1#„

f 2

b

#„n#„x

#„y

Figure 7.6: Ellipse with focal points#„

f 1,#„

f 2 and minor axis b [Are+15, p. 780].The point #„x denotes the position of a pedestrian and #„n denotesthe normal vector on the ellipse.

[Bit17]. Similarly, to the interaction force between pedestrians, theRectangles

repulsive force of cars#„

f cark→i requires the calculation of the point (

#„

bp,i)on the rectangle, which is closest to another ( #„xp,i).

The edges have to be analyzed for the graph routing in the tacticalmodel. Depending on the underlying areas of an edge, it’s weight isestimated. Figure 7.7 shows an edge of a graph with two polygons

n1 n2

Figure 7.7: Splitting an original segment by the means of polygons.

representing different area types. To determine the distances of eacharea type, the functionality was added to obtain a list of subsegments,which result by splitting the original segment with a polygon.

7.4.4 Visualization

Different data can be written to output files. This includes the posi-tions of pedestrians and data for analyzing, such as xt-densities. To in-terpret the simulated scenarios, the visualization package containsa GUI, which is reads the layout file and output files [Kie+16, p. 13].The visualizer was expanded to support the visualization of cross-walks, sidewalks, and footways. As shown in Figure 7.8, the function-ality of visualizing the movement of cars was also added.

7.4.5 Tactical Model

The tactical model was implemented by utilizing the preexisting func-tionality of the framework. Since the functionality of graphs and a Di-jksta algorithm were already implemented [Kie+16], the realization ofthe tactical model was achieved by only implementing the calculation

7.4 momentumv2 63

Figure 7.8: 3D view of MomenTUMv2’s visualizer with sidewalks and crosswalks in light blue. Thecars are represented as dark blue boxes.

of the edge weights. Therefore, the Euclidean distances of the edgesand the pedestrian dedicated shares thereof are processed during thepre-processing phase. Then, the weights are updated according to theindividual factor si for each routing phase. Listing 7.1 shows an ex-emplary configuration of the tactical model with the parameters µs

and σs.

Listing 7.1: XML configuration of the tactical model.

1 <routingModels>

2 <routing id="5" name="dijkstraPerceivedCost" type="

DijkstraPerceivedCost">

3 <property name="perceivedCostMean" type="Double" value="0.3"/>

4 <property name="perceivedCostDeviation" type="Double" value="0.2"/>

5 </routing>

6 </routingModels>

7.4.6 Operational Model

The forces of the operational model, which were discussed in Sec-tion 6.3, depend on the pedestrian’s perception. If a car or another

γv rvpedestrian is in the visual range of the model, a repulsive force is ex-erted. Therefore, a perception model was implemented, which exam-ines whether the object of interest is located within the visual radiusrv and within the visual angle γv. Listing 7.2 gives an exemplary con-figuration of the perception model.

64 implementation

Listing 7.2: XML configuration of the perception model.

1 <perceptualModels>

2 <perceptual id="0" name="SightCone" type="SightCone">

3 <property name="radius" type="Double" value="30.0"/>

4 <property name="angle" type="Double" value="120.0"/>

5 </perceptual>

6 </perceptualModels>

The implementation of the operational model is achieved by con-forming to the interfaces and processing the provided information,such as car data, pedestrian areas, and obstacle lists. The operationalbehavior model is called for each pedestrian and for each simulationstep. Thus, the walking state, which consists of position, velocity, andheading information is updated during each cycle. As explained inSection 6.3, the acceleration of each pedestrian is modelled by themeans of social forces. To improve readability, each social force isimplemented in a dedicated function.

Listing 7.3 shows the exemplary configuration of the operationalmodel. Furthermore, a list of parameters is also given in Table B.1. Tocompare the impact of social forces, the model supports the replace-ment of pedestrian interaction force with the version proposed byHelbing et al. [Hel+00]. Therefore, the first part of the configurationconsists of the parameters required for social force model of Helbinget al. [Hel+00] and the second part comprises the parameters of theoperational model, which was discussed in Section 6.3.

Listing 7.3: XML configuration of the walking model.

1 <walkingModels>

2 <walking id="3" name="socialForceZeng" type="SocialForceZeng">

3 <property name="relaxation_time" type="Double" value="0.5"/>

4 <property name="physical_interaction_kappa" type="Double" value="1.4e5"/>

5 <property name="physical_interaction_k" type="Double" value="0.2e5"/>

6 <property name="panic_degree" type="Double" value="0.0"/>

7 <property name="mass_behaviour_A" type="Double" value="29.0"/>

8 <property name="mass_behaviour_B" type="Double" value="0.04"/>

9 <property name="pedestrian_interaction_helbing_koester" type="Boolean" value="False"/>

10

11 <property name="conflped_strength_angular_effect" type="Double" value="0.7"/>

12 <property name="conflped_interaction_strength" type="Double" value="1.28"/>

13 <property name="conflped_interaction_range_relative_distance" type="Double" value="1.4"/>

14 <property name="conflped_interaction_range_relative_time" type="Double" value="0.51"/>

15 <property name="car_repulsive_interaction_strength" type="Double" value="0.86"/>

16 <property name="car_repulsive_interaction_range" type="Double" value="0.36"/>

17 <property name="crosswalk_repulsive_interaction_strength" type="Double" value="0.35"/>

18 <property name="crosswalk_repulsive_interaction_range" type="Double" value="2.65"/>

19 <property name="crosswalk_attractive_interaction_strength" type="Double" value="0.25"/>

20 <property name="crosswalk_attractive_interaction_range" type="Double" value="0.46"/>

21 </walking>

22 </walkingModels>

8D ATA S E T

In order to compare the implemented behaviour model to the reality,a real-world dataset is required. As the pedestrian model is addition- Requirements

ally influenced by cars, such a dataset should contain the positionsand directions of both. Another requirement constitutes the modelledintersection situation.

8.1 ko-per intersection dataset

The research initiative “Ko-FAS” aimed at increasing traffic safety bythe means of cooperative systems and ran from 2009 to 2013. The ini-tiative consisted of three sub-projects, whereas “Ko-PER” was one ofthem. In order to provide a full all-round vision at crossings with Ko-FAS, Ko-PER

poor visibility, infrastructure was equipped with multiple sensors.The goals of Ko-PER included the early provision of information tothe driver about potential hazardous situations. Furthermore, advi-sory warnings should be provided by the system [Wer14, p. 13].

The sensors were installed at a signalized crossing in Aschaffen-burg, Germany and is shown in Figure 8.1. Four laserscanners record-

Figure 8.1: Public crossing in Aschaffenburg [Sch+14, p. 123; Str+14, p. 1].

ing with 12.5 Hz and eight cameras operating with 25 Hz were de-ployed to record three scenes. The provided data of the first scenecomprises information for each object, such as position, velocities, ac-celeration, sizes, and angles. For the second and third scene, only Sensors and

recorded datareference data for two cars was extracted. In order to reduce the filesizes, the first sequence was divided into four sub-sequences, whereasa list of tracked objects is given in Table 8.1. Due to the division of thedataset, the same pedestrians are recorded across sub-sequences. The

66 dataset

Table 8.1: Objects in the first sequence, which was recorded for 6:28 min[Str+14].

Sequence Cars Trucks Pedestrians Bikes Duration [s]

1a 63 1 10 0 96

1b 63 3 13 3 96

1c 81 5 7 3 96

1d 83 3 8 4 97

complete dataset is provided by the Universität Ulm and is availableat their webpage. 55 http://www.uni-

ulm.de/in/mrm/

forschung/

datensaetze.html

Since the dataset comprises only a limited number of pedestrians,it might not be sufficient for statistical significance. However, thisdataset qualifies for qualitatively comparing the dynamics of realpedestrian to the implemented model. Furthermore, the accuracy ofthe Ko-PER’s dataset is beneficial, as the implemented operationalmodel describes movement patterns of pedestrians.

8.2 dataset preparation

To simulate pedestrian behavior for this crossing with MomenTUMv2,the dataset had to be prepared. Therefore, the layout of the intersec-tion was redrawn in AutoCAD, as depicted in Figure 8.2.

Figure 8.2: Ko-PER intersection drawn in AutoCAD. The blue polygons de-note obstacle and the orange areas represent crosswalks and side-walks. Furthermore, the generator and absorber fields are col-ored green and red.

In order to compare and simulate pedestrian behavior with simi-lar environment, the trajectory data of pedestrians, cars, and trucks

http://www.uni-ulm.de/in/mrm/forschung/datensaetze.html




8.2 dataset preparation 67

were extracted from the dataset. This was achieved by the means ofthe provided DataSetViewer, which is a Matlab program to view andprocess the dataset [Str+14]. The data preparation included reindex-ing of the objects and recalculation of time reference points as well asposition reference points.

The resulting pedestrian trajectories of the four sub-sequences arevisualized in Figure 8.3.

(a) Sequence 1a: 10 pedestrian trajectories (b) Sequence 1b: 13 pedestrian trajectories

(c) Sequence 1c: 7 pedestrian trajectories (d) Sequence 1d: 8 pedestrian trajectories

Figure 8.3: Trajectories of the Ko-PER dataset [Str+14].

Part III

C O N C L U D I N G D I S C U S S I O N

9D I S C U S S I O N

The pedestrian behavior model is qualitatively assessed and com-pared to the Ko-PER dataset in the following. Potential standardiza-tion areas and their accompanied attributes are discussed afterwards.

9.1 pedestrian simulation model

In order to simulate the pedestrian behavior, the parameters µs andσ2

s of the tactical model have to be estimated first. However, the Ko-PER dataset does not provide a sufficient quantity to estimate thetwo parameters, since it only consists of trajectories of 24 individualpedestrians. Furthermore, 15 pedestrians do not face an ambiguousdecision of whether to route over the street or over sidewalks/cross-walks. This is due to the fact, that their trajectories start and end onsidewalks and their shortest routes is located on side- and crosswalksanyway. Thus, the parameters are discussed by means of an exem-plary scenario, in which the routing is influenced by their perceivedrisk tolerance.

9.1.1 Tactical Model

The parameters describe the normal distribution of the factor, whichmodels the decreased risk of a pedestrian taking a sidewalk or cross-walk, as stated in Equation 6.3. Therefore, an exemplary scenario is

(a) σs = 0.1 : pa = 59.9 %, pb = 49.1 %, pc = 0 % (b) σs = 0.2 : pa = 55 %, pb = 41.6 %, pc = 3.4 %

Figure 9.1: Trajectories of pedestrians simulated with µs = 0.2.

simulated in which the routing is influenced by the factor si, which isnormally distributed by N (µs, σ2

s ).

72 discussion

(c) σs = 0.3 : pa = 53.3 %, pb = 35.5 %, pc = 11.2 % (d) σs = 0.4 : pa = 52.5 %, pb = 29.4 %, pc = 18.1 %

Figure 9.1: (Continued) Trajectories of pedestrians simulated with µs = 0.2.

In this scenario pedestrians start at the south-west and have to crossthe street once to reach their destination, which is located in the eastof the layout. As depicted in Figure 9.2d, the pedestrians have threemajor routes to reach the destination:

(a) si < 0.225: routing over the three crosswalks constitutes thelowest risk and longest distance.

(b) 0.225 ≤ si < 0.565: routing over the sideways and crossing thestreet once.

(c) 0.565 ≤ si: routing over the sidewalk and diagonally crossingthe street constitutes the highest risk.

Figure 9.1 and Figure 9.2 show the simulated trajectories for µs =

0.2 and µs = 0.3, respectively. Thereby, the variable pi denotes theprobability of a pedestrian taking the route i ∈ {a, b, c}.

Figure 9.1a and Figure 9.2a show a very low probability of 0 % and0.4 %, that pedestrians take the diagonal route c. For the followingdiscussion of the operational model in Section 9.1.2, it is assumedthat the majority of pedestrians would take the route b and roughly athird of pedestrians choose the route over the three crosswalks. Thisassumptions suggests the parameters µs = 0.3 and σs = 0.2.

Clearly, the tactical model and its assumed parameters are not ad-dressing complete pedestrian behavior, which can be found in realtraffic scenarios. First, the tactical behavioral level does not modeltraffic light phases. Therefore, pedestrians are not waiting in front ofcrosswalk in case of a red traffic light phase. Zeng et al. model theTraffic lights

waiting by adjusting the driving force of the operational level. Thedriving force is influenced by a binary stop or go decision [Zen+17,pp. 46 sq.]. However, it could be beneficial to model this pedestrianbehavior on the tactical behavioral level, since this not only conformsto Hoogendoorn et al.’s categorization [Hoo+01] but also conforms

9.1 pedestrian simulation model 73

(a) σs = 0.1 : pa = 22.7 %, pb = 76.9 %, pc = 0.4 % (b) σs = 0.2 : pa = 35.4 %, pb = 55.4 %, pc = 9.3 %

(c) σs = 0.3 : pa = 40.1 %, pb = 41.0 %, pc = 18.9 % (d) σs = 0.4 : pa = 42.6 %, pb = 32.1 %, pc = 25.4 %

Figure 9.2: Trajectories of pedestrians simulated with µs = 0.3.

to MomenTUMv2’s framework approach [Kie17, pp. 162 sq.]. Depend-ing on a pedestrian’s context and his perception, a tactical model ischosen. These models include routing models, searching models, par-ticipating and queuing models. To model the pedestrian behavior dy-namics of waiting in front of red signal phases, a waiting or queuingmodel needs to be implemented.

The second oversimplification constitutes the routing based on theEuclidean distance and a normally distributed perceived cost on ar-eas, which are dedicated to pedestrians. A pedestrian crossing inunsignalized situations involves more influencing factors. This in- Additional

influencing factorscludes locations where pedestrians are more likely to cross the streetand the interaction between driver and jaywalkers [Zhe+15; ZE17].However, the topic of pedestrian-vehicle interactions outside of cross-walks comprises another research topic.

Nevertheless, the tactical model describes routing over areas, whichare preferred by pedestrians, such as crosswalks and sidewalks. Fur-thermore, a pedestrian is modelled to individually evaluate the situa-tion and potential routes.

74 discussion

9.1.2 Operational Model

In the following, the modelled operational behavior is qualitativelycompared to the pedestrians, which were tracked and recorded inthe Ko-PER dataset. Therefore, a selection of scenarios is structuredaccording to the model’s social forces, which were explained in Sec-tion 6.3.

The utilized parameters are the result of Zeng et al.’s calibration.Zeng et al. collected trajectory data on an intersection by the meansof a quadrocopter for a period of one hour. The trajectories were semi-automatically extracted. Then, Zeng et al. calibrated the social forceby applying a genetic algorithm to minimize relative errors. Thereby,Zeng et al.’s

calibration the set of parameters are optimized for three different fitness func-tions. The first fitness function minimizes the relative distance errors,which denote the distance between observed and modelled trajec-tory points. Further, the second minimizes the relative angular errors,which denote the angle between the next observed position and thenext simulated position. The third fitness function describes the meanof the relative distance error and relative angular error [Zen+17, p. 51].To equally address the importance of the angular and distance error,the parameter set of the third fitness function was utilized for thefurther discussion and is listed in Table B.1.

Pedestrian Interaction

The pedestrian interaction force models the desired private sphereand the avoidance of collisions. Figure 9.3 shows a situation where

Figure 9.3: Sequence 1b of the Ko-PER dataset with trajectories [Str+14].

two groups of two pedestrians walk towards south-west and onepedestrian is approaching the crosswalk from the other side. Further-more, a social group four pedestrians is waiting at the southern cor-ner of the intersection for a green traffic light phase.

When comparing the situation from the dataset to the simulatedresults, shown in Figure 9.4, several differences have to be pointed


out. The implemented model does not describe the forming of socialgroups and their dynamics. However, as shown in Figure 9.3, socialgroups impact the trajectories, since pedestrians form different grouppatterns [Gor+16]. The Ko-PER trajectories show a higher spatial de-gree of dispersion, as the pedestrians within a group walk side byside.

(a) Pedestrian interaction force proposed by Zeng etal. [Zen+17]

(b) Pedestrian interaction force by Helbing et al.[Hel+00] with modifications by Köster et al.[Kö+13] to improve robustness

Figure 9.4: Simulated situation, which is similar to the sequence 1b of the Ko-PER dataset.

In Zeng et al.’s model pedestrians are represented as points. Fur-ther, it is assumed, that the repulsive interaction force is only exerted,if the velocity vectors form a collision point [Zen+17]. However, theseassumptions do not cover all situations accurately. An exemplary sit-uation is depicted in Figure 9.5. Since the body radius is neglected inthe model and the velocity vectors are parallel, this situation will notentail a repulsive force on either of the two pedestrians. Therefore,

Figure 9.5: Pedes-trians right beforea collision.

the repulsive behavior between pedestrians is not addressed in all sit-uations, whereas the situation depicted in Figure 9.4a illustrates oneof them.

Figure 9.4b illustrates the same scenario, whereas the interactionforce is replaced by Helbing et al.’s version of it [Hel+00, p. 3]. Incontrast, this force takes the body radius into account and does notmodel potential collisions by means of intersecting velocity vectors.As depicted in Figure 9.4b, the pedestrians perform evasive manoeu-vres and therefore the green bodies of the pedestrian do not collide.

Crosswalk Interaction

The crosswalk interaction force models the tendency of pedestriansto walk inside of crosswalks. This relation is also supported by thepedestrians, who are crossing the north-western street in the Ko-PERdataset, as shown in Figure 9.6. Apart from two pedestrians, the ma-jority of the all pedestrian trajectories is located within the crosswalk.

76 discussion

(a) Sequence 1a (b) Sequence 1b (c) Sequence 1c (d) Sequence 1d

Figure 9.6: Pedestrian trajectories of the Ko-PER dataset.

One pedestrian in Figure 9.6d is taking a shortcut towards his destina-tion in north. The second exception constitutes the pedestrian, who isalmost walking on the boundary of the crosswalk in Figure 9.6c. Fur-thermore, the trajectories suggest that pedestrians tend to enter andleave the crosswalk area with slight shortcuts to lessen the walkingdistance.

In order to simulate the pedestrian behavior at crosswalks, pedes-trians walk from the northern origins towards the south by crossingthe same street as in Figure 9.6. Thereby, Figure 9.7a depicts the gen-

(a) Routing graph (b) Trajectories of simulated pedestrians crossing thestreet

Figure 9.7: Trajectories of simulated pedestrians crossing the street.

erated graph by which pedestrians route towards their destination.The driving force of a pedestrian is directed towards the next routednode. Figure 9.7b shows a simulated scenario with five pedestrianscrossing the street. Here, the crosswalk force attracts the pedestriantowards the middle of the crosswalk and therefore the pedestrians’trajectories slightly deviate from the edges of the graph. Figure 9.8shows a plot of the different social forces for the selected pedestrian(red) in Figure 9.7b. Since the selected pedestrian walks diagonally


20 22 24 26 28time [s]

0.0

0.1

0.2

0.3

0.4

0.5

magnit

ude o

f so

cial fo

rce [

m/s

^2

]

Driving Force

Pedestrian Interaction ForceCrosswalk Force

Figure 9.8: Magnitude of social forces, which are exerted on the red pedes-trian in Figure 9.7b.

on the crosswalk, the magnitude of the crosswalk forces are strongerwhen entering and leaving. The reason for the spike at 19.2 s is the dis-tance jump from the one boundary to the other. This distance changeimpacts the social force, as stated in Equation 6.18.

The comparison between the dataset trajectories and the simulationtrajectories suggests that real pedestrians show more path variability.As mentioned before, one reason for this are social group dynamics.However, another reason is the graph, which shows a limited numberof edges traversing the crosswalk. Modelling a more realistic crossingbehavior could involve a graph generation, which produces edgesrepresenting the short entering and leaving shortcuts.

Car Interaction

This interaction force models the repulsive effect of cars on pedestri-ans. In order to compare the modelled effect, a pedestrian crossing

Figure 9.9: Pedestrian crossing the street without cars located nearby.

the street is simulated with and without nearby cars. The trajectoryof a pedestrian in absence of other cars is shown in Figure 9.9. In con-trast, Figure 9.10 shows a pedestrian and his trajectory with nearby

78 discussion

cars. The model parameters, such as desired velocity, route choice,origin and destination are identical over the two simulations.

(a) Simulated situation at 72 s (b) Simulated situation at 75.5 s

(c) Simulated situation at 79 s (d) Simulated situation at 80 s

Figure 9.10: Repulsive effect of a starting car.

In this scenario, the cars are waiting until 79 s and then start movingtowards south-east. The pedestrian is heading towards south-westand his originally planned trajectory is near the simulated cars. Sincethe pedestrians are in his visual range and rather close, the pedestrianalters his trajectory compared to Figure 9.9. As shown in Figure 9.10,the pedestrian keeps a certain distance between him and the car bywalking closer to the middle of the crosswalk.

Figure 9.11 shows a graph of the forces exerted on the pedestrianduring traversing the crosswalk. The repulsive force increases as thepedestrian approaches the vehicles and the sudden drops are caused,since a certain car is not in the field of vision any longer. Furthermore,the strength of the driving force also increases, so that the pedes-trian reaches his next node on the routing graph. Here, the questionarises of whether the perception of a pedestrian is realistically mod-elled by assuming a sight cone. Clearly, this assumption neglects a

9.2 standardization potentials 79

70 72 74 76 78 80 82time

0.0

0.1

0.2

0.3

0.4

0.5

0.6Driving Force

Pedestrian Interaction Force

Car Interaction Force

Crosswalk Force

magnit

ude o

f so

cial fo

rce [

m/s

^2

]

Figure 9.11: Magnitude of social forces, which are exerted on the pedestrianin Figure 9.10.

pedestrian’s auditory perception of vehicles. Nevertheless, the imple-mented repulsive effect of cars suggest a realistic pedestrian behavior.

9.2 standardization potentials

As discussed in Chapter 4, the performance of an automated drivingsystem can be tested by virtually exposing the system to controlledtraffic scenarios. In order to validate and safeguard automated driv-ing systems, standards are necessary to ensure system performanceand failsafe operation. This standardization need was formulatedby the VRA consortium [Veh13, p. 41] and the development of suchstandardized testing methods constitutes the goal of the PEGASUSproject, which was discussed in Section 4.2 [PEG17].

The standardization areas for testing automated driving systemscan be generally categorized into compatibility and quality standards.In order to simulate different scenarios in various situations, a lan-guage is required to describe the logical map data, which containsroad networks, infrastructural elements, road surfaces, buildings, andso on. A map standard clearly entails network effects for the involvedadopters. Developers of simulation software, cartographers, testing Static content

descriptionstandardization

facilities benefit from an industry-wide standard, as it facilitates theexchange of digitally represented maps. Since the testing of driverassistance systems is not new, an already existing standard couldbe adjusted for the requirements of testing automated driving sys-tems. The likelihood of adapting an existing map standard dependson the sunk investments and switching costs of complementary prod-ucts and knowledge. The OpenDRIVE standard is directed towardsthis demand and is developed by the firm VIRES Simulationstechnolo-gie GmbH, which also develops the driving simulator VTD [VIR15].Clearly, a widespread adoption of the OpenDRIVE standard stimu-

80 discussion

lates the demand for complementary products, such as VIRES’s driv-ing simulator.

Map standards comprise the static content of a simulation, whereasthe description of the dynamic content includes the parameterizationof the utilized models, initial positions, and scenario conditions, suchas weather. The PEGASUS project has further identified requirementsfor a unified language to describe the dynamic content of scenar-ios. The requirements include the compatibility with test specifica-Dynamic content

descriptionstandardization

tion databases, as well as the instantiation of specific scenarios fromthe database [VIR16, p. 35]. VIRES is again addressing this demandby developing an open standard named OpenSCENARIO [VIR17a].As VIRES is also a member of the PEGASUS project, they are inthe strategic position to directly collect standardization needs, whichare discussed within the PEGASUS project. Since stakeholders fromseveral areas, such as manufacturing, automotive suppliers, research,and test labs, are represented in the PEGASUS project, a mutual un-derstanding between those industries is gained. Moreover, the PEGA-SUS project does not only provide valuable technical input for VIRES,but also offers access to potential adopters, which are necessary to es-tablish a de facto standard in the market. VIRES can benefit from thePEGASUS project, as this project has the potential to trigger a band-wagon effect for the OpenSCENARIO standard due to the provisionof a larger installed base. Clearly, the network externalities comprisethe benefit of a simplified exchange of simulator-agnostic scenariodescriptions.

Influence of the driver behavior in the controllability assessment

-3-

Database concept

The database concept consists of different database entities following the data processing

from (raw) measurement data over abstracted scenario clusters (logical scenarios) to test

specifications for the sign-off process, see Figure 2. The basic idea is to use data from differ-

ent sources (see section Data sources for the database), group the scenarios in this data to log-

ical scenarios and to derive the test specifications for the sign-off process based on the logical

scenarios. Figure 2 depicts the related database entities and their connecting data processing

chain, which is described in section Data processing chain for the database.

In this data processing the coverage of the scenario information (y-axis) increases with the

different database entities: While the raw measurement data provides only selective infor-

mation on possible scenario characteristics for the logical scenarios, the condensation of this

information in the parameter space of the logical scenarios enhances the knowledge of possi-

ble parameter combinations within a logical scenario. Deducing the test specifications infor-

mation on exposure, potential severity and controllability are added for the parameter space

improving the information coverage of the scenarios.

At the same time as the coverage of scenario information increases, the data volume is re-

duced (x-axis), especially between raw data and logical scenarios. Raw data contains also

information that is of secondary importance for the logical scenarios and the parameter space.

However, if it is necessary to assess detailed information it is always possible to trace back

between test specification, logical scenario and raw data.

Data processing chain

Post processing of

individual scenarios

e.g. for individual case

assessment, function development, etc.

Data volume reductionlow high

Co

ver

age

of

scen

ario

in

form

atio

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igh

Data processing chain

Requirement definition and filtering criteria

External data

Use case definition

• Definition of functional

scope of HAD

• Definition of scenario filter

0

Scenario searching and

clustering

• Scenario clustering

• Combined scenarios with frequencies

• User specific retrace on

single scenarios

6

Σ

Szenario ID

Fre

qu

en

cy

min TTC (s)

Fre

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Raw data

Scenario

characterization

• Cut to scenario snippets

of likelihoods > 95%

• Calculation of

indicators for the scenarios

5

Time (s)

Lik

elihood

0 -

1 -

0

ID2ID1

Calculation of scenario

affiliation

• Calculation of

affiliation likelihoods of situations to a specific

scenario over time

4

Time (s)

Lik

elihood

0 -

1 -

0

scenario1scenario2

Generation of deduced

signal

• Raw data enrichment

with deduced signals

3

Time (s)

De

du

ce

d s

ign

als TTC

Data transformation

• Format checks

• Indexing

• Assignment of access rights

2Generation of common

environment and traffic

description

• Harmonization of signal names

• Transformation in

common data format

by data owner1

Zeit (s)

Scenari

o v

ari

able

speed

lane position

E / S / C

Parameter

space

Test

specifications

Test specification

deduction

• Select scenarios based

on functional scope • Add information on …

• Exposure

• Severity

• Controllability

for sections of the parameter space

7

min TTC (s)

E / S

/ C

Parameter

space

Logical

scenario

E1 E3 E2

S4 S3

C0 C1 C2

Figure 2: Database concept with different database entities and data processing chain

Figure 9.12: Database testing concept to validate highly automated driving systems with relevanttraffic scenarios [Pü+17, p. 3].

Figure 9.12 shows a concept to test highly automated vehicles bymeans of a database validation framework, which is developed bythe PEGASUS project. Thereby, an essential challenge constitutes the

9.2 standardization potentials 81

identification of relevant traffic scenarios and model parametrizations,by which automated driving systems shall be tested. In order to en-sure the safety of automated driving systems from all manufacturers,the simulation scenarios have to be specified and could constitute apart of the vehicle’s sign off process. Specifying a catalog of simula- Standardized test

scenarios andcriticality metrics

tion scenarios belongs to the group of minimum attributes standardsand aims at covering all necessary traffic situations and eventuali-ties. Step 7 in Figure 9.12 represents the deduction of the simulatedtest cases. The performance evaluation of an automated driving sys-tem requires metrics, which estimate the criticality of the situations.Thus, the passing or failing of test cases requires the standardizationof values that may not be exceeded or fallen short of. However, thestandardization of test catalogs and criticality metrics depends on thecountry’s traffic regulations and its culture.

10C O N C L U S I O N A N D O U T L O O K

Pedestrians and the protection of them will play an essential role in re-alizing and establishing automated driving. According to the EthicsCommission, the deployment of automated driving systems is onlyjustifiable, if such systems are capable of handling critical situationsmore reliable than human drivers and therefore improve the safetyfor all road users [Eth17, p. 10]. Thus, the safety verification of auto-mated driving systems constitutes not only a technical developmentchallenge, but is also subject to standardization efforts. Since auto-mated driving systems of all vehicle manufacturers pose a potentialrisk to other road users, standardized testing procedures are underdevelopment to ensure vendor-agnostic safety on the roads.

The intention of this work was to investigate the different standard-setting dynamics related to the area of automated driving. Therefore,the key players of the involved industries were identified and theirmemberships in SDOs, consortia, and SIGs analyzed. Compatibilitystandardization fields, such as terminology, digital mapping, internalcar and C2X communication were portrayed. Furthermore, initiativesof the EU and of Germany to develop quality standards for ensur-ing safety of automated driving systems were discussed. However,the conducted work is more directed towards creating an overview,rather than describing an in-depth view of standardization dynamics.In order to acquire an in-depth perspective of a certain standardiza-tion process within a standards body, a qualitative research could becarried out as a next step. Therefore, interviews of stakeholders in dif-ferent positions, meeting protocols, technical solution proposals, andmailing lists could contribute to a content analysis.

In order to verify the safety of automated driving systems fromdifferent vendors, standardized simulation tests have the potentialto play a significant role, as they enable the efficient testing of vastnumbers of traffic scenarios under controlled environments. Hereby,pedestrian behavior simulation is particularly crucial for urban sce-narios, since pedestrians are directly exposed to malfunctioning driv-ing systems. Therefore, the second part of this thesis consisted ofdeveloping a pedestrian behavior model, which describes the inter-action between pedestrians and cars. The pedestrian behavior modelwas qualitatively assessed by comparing the simulated behavior re-sults to recorded pedestrian behavior of the Ko-PER dataset. Futureimprovements could involve the description of social group behavioras well as a more differentiated contextual situation awareness.

Part IV

A P P E N D I X

AC O M PA N I E S P E R I N D U S T RY

a.1 automotive

Table A.1: 20 largest vehicle manufacturers with their brands by produced units in 2016 [Org].

Country Manufacturer Units 2015 Brands Source

Asia

China BAIC Group 1 169 894 BJ, New Energy Vehicle, Senova,Wevan

[BAI17, p. 10]

Changan Automobile 1 540 133 Changan [Cho17]

Dongfeng 1 209 296 Dongfeng, Liuzhou, Venucia [Don17, p. 50]

SAIC 2 260 579 MG, Maxus, Roewe [SAI17, p. 10]

India Tata Motors 1 009 369 Jaguar, Land Rover, Tata, TataDaewoo

[Tat17, p. 2]

Japan Honda 4 543 838 Acura, Honda [Hon17, p. 5]

Mazda 1 540 576 Mazda [Maz17]

Mitsubishi Motors 1 218 853 Mitsubishi [Mit17]

Suzuki 3 034 081 Suzuki [Suz17]

Toyota 10 083 831 Daihatsu, Hino, Lexus, Toyota [Toy17, p. 2]

Nissan 5 170 074 Datsun, Infiniti, Nissan [Nis17, p. 5]

South Korea Hyundai 7 988 479 Genesis, Hyundai, Kia [Hyu17]

Europe

France Groupe PSA 2 982 035 Citroën, DS Automobiles, Opel,Peugeot, Vauxhall

[SAI17]

Renault 3 032 652 Alpine, Dacia, LADA, Renault, [Gro17, p. 12]

Renault Samsung Motors

Italy Fiat Chrysler 4 865 233 Abarth, Alfa Romeo, Chrysler,Dodge, Fiat, Jeep, Lancia, Ram

[Fia17, pp. 40 sq.]

Germany BMW Group 2 279 503 BMW, MINI, Rolls-Royce [Bay17, p. 4]

Daimler 2 134 645 Mercedes, smart [Dai17, p. 93]

Volkswagen Group 9 872 424 Audi, Bentley, MAN, Porsche,SEAT, Scania, Volkswagen,ŠKODA

[Vol17, p. 2]

North America

USA Ford 6 396 369 Ford, Lincoln [For17]

General Motors 7 485 587 Buick, Cadillac, Chevrolet,GMC, Holden, Wing

[Gen17]

88 companies per industry

Table A.2: 20 largest OEM parts supplier by sales of automotive original equipment parts in 2016

[Aut17b].

Continent Country Company OEM part sales

($ in millions)

Asia China Yanfeng Automotive Trim Systems Co. 12 991

Japan Aisin Seiki Co. 31 389

Denso Corp. 36 184

JTEKT Corp. 10 778

Panasonic Automotive Systems Co. 11 988

Sumitomo Electric Industries 12 835

Yazaki Corp. 15 600

South Korea Hyundai Mobis 27 207

Europe France Faurecia 20 700

Valeo SA 17 384

Germany Continental AG 32 680

Mahle GmbH 12 173

Robert Bosch GmbH 46 500

Schaeffler AG 10 883

Thyssenkrupp AG 10 986

ZF Friedrichshafen AG 38 465

North America Canada Magna International Inc. 36 445

USA Adient 16 837

Delphi Automotive 16 661

Lear Corp. 18 558

A.2 telecommunication 89

a.2 telecommunication

Table A.3: Telecommunication equipment makers by global market share in2015 [KDB15].

Continent Country Company Market share

Asia China Huawei 24 %

ZTE 7 %

South Korea Samsung 3 %

Europe Sweden Ericsson 33 %

Finland Nokia Corporation 29 %

Table A.4: 20 largest telecommunication service companies by revenue [For].

Continent Country Company Revenue

($ in billions)

Asia China China Mobile 106.8

China Telecom 53.0

Japan KDDI 43.2

Nippon Telegraph & Tel 105.0

Softbank 82.1

Saudi Arabia Saudi Telecom 13.8

Singapore SingTel 11.9

United Arab Emirates Etisalat 14.3

Australia Australia Telstra 18.6

Europe France Orange 45.3

Vivendi 12.0

Germany Deutsche Telekom 80.9

Italia Telecom Italia 21.0

Spain Telefónica 57.6

United Kingdom BT Group 31.7

Vodafone 60.8

North America Canada BCE 16.4

USA AT&T 163.8

Verizon Communications 126.0

South America Mexico América Móvil 52.2

90 companies per industry

a.3 navigation and mapping

Table A.5: List of map data provider.

Continent Country Company Source

Asia China Baidu [Bai17]

NavInfo [Nav17a]

Japan Zenrin [ZEN17]

Europe Netherlands HERE [HER17]

TomTom [Tom17]

North America USA Google, Waymo [Bou17]

BM O D E L PA R A M E T E R S

Table B.1: Parameters used for simulations, if not stated otherwise. The operational parameterswere estimated by Zeng et al. [Zen+14b; Zen+17].

Model Var. Range Unit Value Description

Tactical

µs 0.3 Mean of perceived cost factor

σs 0.2 Standard deviation of perceived cost factor

Operational

Visual rv m 30 Visual range

γv ° 120 Visual opening angle

Driving τp [0.5, 4.5] s 0.5 Relaxation time

vd [0.9, 1.1) m s−1 2.5 % Desired velocity distribution

[1.1, 1.3) m s−1 2.5 %

[1.3, 1.5) m s−1 5.5 %

[1.5, 1.7) m s−1 22 %

[1.7, 1.9) m s−1 39 %

[1.9, 2.1) m s−1 17 %

[2.1, 2.3) m s−1 8 %

[2.3, 2.5) m s−1 3 %

[2.5, 2.7) m s−1 1 %

[2.7, 2.9) m s−1 1 %

Conflicting pedestrian λα [0, 1] Strength of angular effect

Arβ [0.1, 2.0] m s−2 1.28 Interaction strength for repulsive force

Brβ [0, 16] m−1 1.42 Interaction range for relative distance

Brβα [0.1, 0.8] m−1 0.51 Interaction range for relative conflict time

Car Ac [0.1, 1.6] m s−2 0.86 Interaction strength

Bc [0.1, 0.8] m−1 0.36 Interaction range

Crosswalk Arb [0.1, 0.6] m s−2 0.35 Interaction strength for repulsive force

Brb [0.1, 5.0] m−1 2.65 Interaction range for repulsive force

Aab [0.1, 0.45] m s−2 0.25 Interaction strength for attractive force

Bab [0.1, 1.7] m−1 0.46 Interaction range for attractive force

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D E C L A R AT I O N

I hereby declare that the thesis submitted is my own unaided work.All direct or indirect sources used are acknowledged as references.

I am aware that the thesis in digital form can be examined for theuse of unauthorized aid and in order to determine whether the thesisas a whole or parts incorporated in it may be deemed as plagiarism.For the comparison of my work with existing sources I agree that itshall be entered in a database where it shall also remain after exam-ination, to enable comparison with future theses submitted. Furtherrights of reproduction and usage, however, are not granted here.

This paper was not previously presented to another examinationboard and has not been published.

München, November 30, 2017

Benedikt Schwab

Everything will be okay in the end.If it’s not okay, it’s not the end.

John Lennon

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This document was typeset using the typographical look-and-feelclassicthesis developed by André Miede. The style was inspiredby Robert Bringhurst’s seminal book on typography “The Elements ofTypographic Style”.

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