User needs and operational requirements for MiniFaros ......assistance system Deliverable No. D3.1...

Deliverable 3.1 PUBLIC MiniFaros FP7-ICT-2009-4_248123

MINIFAROS_D3.1_Requirements_and_user_needs_v10.doc Page 1 of 45

MINIFAROS Small or medium-scale focused research project

FP7-ICT-2009-4_248123

User needs and operational requirements for MiniFaros

assistance system

Deliverable No. D3.1

Workpackage No. WP3 Requirements and User Needs

Task No. A3.1 - A3.3

3.1 Accident Review and Relevant scenarios, 3.2 User needs 3.3 Sensor Requirements

Coordinator Kay Fuerstenberg, SICK AG

Authors Torbjörn Johansen, Volvo Technology Corporation, VT EC; Radim Hrabica, SKODA AUTO a.s., SKO; et altera

Status: Public

Version No: 1.0

File Name: MINIFAROS_D3.1_Requirements_and_user_nee ds_v10.doc

Issue Date: 15 October 2010

Project start date and duration 1 January 2010, 36 Months



EXECUTIVE SUMMARY The MiniFaros project vision is to enable accident-free traffic by the use of effective environment perception systems. Laser scanners are the predominant generic environment sensing technology.

The project aims to develop a low cost novel miniature laser scanner for advanced driver assistance systems, ADAS, for vehicles. The developed laser scanner aims to be affordable, small and lightweight in order to significantly increase the penetration of ADAS in Europe.

ADAS functions have been developed in several European projects during the last decade but the penetration rate is still very low on the market. One reason for this is that these functions are relatively costly. The ADAS functions have so far almost only been introduced in the full-size luxury car class segment. Most of the vehicles in Europe however are mid-size and smaller classes where these functions have not yet been introduced or demanded. A low cost, small size laser scanner would certainly alter this.

To reach the objectives of small and low cost laser scanner a number of new techniques have to be developed and evaluated.

The rotating mirror in laser scanners has previously been realized with a macro mechanical scanning system. This relatively large moving part in the sensor has not been fully accepted by OEMs even though it has been proved reliable. The use of a MEMS (Micro-Electro-Mechanical system) mirror in the novel laser scanner might alter this. By developing a MEMS mirror for the laser scanner will also enable downsizing of the sensor.

The receiver and the Time-to-Digital-Converter (TDC) are the major integrated circuits in the sensor. These components are essential when reducing the size and cost of the sensor. The integration of these into a common circuit will be an advantage regarding cost and size. It will also enable other benefits like compensation of timing error and a possibility to measure several distances in a single laser pulse that will be beneficial for operation in bad weather conditions like rain and fog.

The use of free-formed optics and aspheric surfaces developed for the laser scanner will reduce the sensor size but enabling a very large field of view. The free-form optics will reduce the number of optical components and lower production cost further.

Accident review and analyses from European accident statistics have been performed and shows that pedestrian protection and pre-crash functions are the main scenarios that can be addressed by an ADAS function using data from the MiniFaros laser scanner. In total around 54% to 82% of all severe accidents for cars and trucks could be addressed by ADAS safety functions that could be using a MiniFaros laser scanner. The laser scanner can also be used for a number of ADAS comfort functions as stop and go and parking assistance.



In this sensor development project exemplary ADAS functions already developed for safety will be adapted to the MiniFaros laser scanner. There have been several European projects aimed for the development of safety functions and a survey of the state-of-the-art ADAS functions have been performed in order to set the requirements on the MiniFaros laser scanner correctly.

The requirements are described in functional terms regarding range, field of view, accuracy et cetera. The state-of-the-art ADAS survey show that the general requirements of the laser scanner sensor would be:

• Range: 80 meters

• Range accuracy: 0.1 meters in near-field and 0.3 meters else

• Field of view: 250 degrees

• Angular accuracy: 0.25 degrees

• Update frequency: 25 Hz

Additionally more general requirements on the laser scanner are also specified because they will have to comply with automotive standards to be fitted and operated in vehicles.

Object recognition algorithms have to be developed for the laser scanner for the safety applications addressed by the sensor. This includes the development of enhanced object recognition and tracking algorithms and performing improved object classification in order to be able to decide the correct strategy for avoiding objects in the traffic environment.

The laser scanner will be shown and demonstrated serving various safety applications in vehicle environment. Both a car and a truck demonstrator will be used. To show the huge potential of the MiniFaros laser scanner in non automotive applications infrastructure based perception will be developed and demonstrated.



Revision Log Version Date Reason Name and Company

0.01 2010-04-13 Document structure Torbjörn Johansen, VTEC 0.02 2010-05-03 First ICCS input Vassilis Kaffes, ICCS

0.03 2010-05-05 First SKO input Radim Hrabica, SKO

0,11 2010-05-25 Compilation of partner input and first version.

Torbjörn Johansen, VTEC

0.12 2010-06-03 Input from SICK Kay Fuerstenberg, SICK 0.13 2010-06-04 Second ICCS input Vassilis Kaffes, ICCS 0.14 2010-06-08 Second SKO input Radim Hrabica, SKO

0.21 2010-06-24 Compilation of partner input and second version Torbjörn Johansen, VTEC

0.22 2010-06-29 Partner input Radim Hrabica, SKO Vassilis Kaffes, ICCS Kay Fuerstenberg, SICK

0.31 2010-07-09 Final document for review Torbjörn Johansen, VTEC

0.32 2010-07-14 Peer review report and document comment

Vassilis Kaffes, ICCS Radim Hrabica, SKO

0.33 2010-07-19 Review input and document comment

Florian Ahlers, SICK

0.41 2010-07-20 Final document proposal Torbjörn Johansen, VTEC

0.42 July, August 2010

Partner input and peer review reports

Florian Ahlers, SICK Radim Hrabica, SKO

0.51 2010-09-02 Compilation of partner input to final document

Torbjörn Johansen, VTEC

0.52 2010-09-05 Peer review report Tapani Mäkinen,VTT 0.61 2010-09-24 Final document proposal Torbjörn Johansen, VTEC

0.62 2010-09-28 Partner comments Axel Jahn, SICK Kay Fuerstenberg, SICK

0.71 2010-09-29 Final document proposal Torbjörn Johansen, VTEC

0.72 2010-09-30 Partner input Kay Fuerstenberg, SICK 0.8 2010-10-15 Final document Torbjörn Johansen, VTEC 1.0 2010-10-15 Final layout Kay Fuerstenberg, SICK



Table of contents EXECUTIVE SUMMARY ............................................................................................................................ 2 Revision Log .................................................................................................................................................. 4 Table of contents ............................................................................................................................................ 5 List of Figures ................................................................................................................................................ 6 List of Tables.................................................................................................................................................. 6 1. Introduction ............................................................................................................................................ 7

1.1. Advanced driver assistance systems overview............................................................................ 9 2. Accident review and relevant scenarios ............................................................................................... 11

2.1. Databases investigated............................................................................................................... 11 2.2. Main results of accident review................................................................................................. 11

2.2.1. Accidents by car segments.................................................................................................... 13 2.2.2. Accidents by road users ........................................................................................................ 14

2.3. Accident review for cars............................................................................................................ 15 2.3.1. Used statistical sample.......................................................................................................... 15 2.3.2. Types of accidents for cars ................................................................................................... 16 2.3.3. Addressed accident types by MiniFaros laser scanner for cars............................................. 18

2.4. Accident review for trucks ........................................................................................................ 19 2.4.1. Types of accidents for trucks ................................................................................................ 19 2.4.2. Addressed accident types by MiniFaros laser scanner for trucks ......................................... 21

2.5. Accident review for pedestrians ................................................................................................ 23 2.5.1. Accident type for pedestrians ............................................................................................... 23 2.5.2. Time of day and road conditions .......................................................................................... 25 2.5.3. Addressed accident types by MiniFaros laser scanner for pedestrians ................................. 25

2.6. Accident review for frontal crash .............................................................................................. 26 2.6.1. Addressed accident types by MiniFaros laser scanner for frontal crashes............................ 27

3. User needs ............................................................................................................................................ 28 3.1. Requirements from the road user............................................................................................... 28

3.1.1. Demographical data for survey on ADAS............................................................................ 28 3.1.2. Results from road user survey on ADAS.............................................................................. 29

4. Sensor requirements ............................................................................................................................. 32 4.1. Functional requirements ............................................................................................................ 32

4.1.1. Sensor output requirements .................................................................................................. 32 4.1.2. Processing output requirements ............................................................................................ 33 4.1.3. Range requirements - object recognition .............................................................................. 34 4.1.4. Range accuracy..................................................................................................................... 35 4.1.5. Field of view......................................................................................................................... 35

4.2. Functional requirements for Infrastructure laser scanner .......................................................... 37 4.2.1. Object detections and measurement ..................................................................................... 37 4.2.2. Estimation of Needed Coverage Area................................................................................... 37 4.2.3. Data acquisition rate ............................................................................................................. 38 4.2.4. Practical Limitations............................................................................................................. 38

4.3. Non-functional requirements..................................................................................................... 38 4.4. General requirements ................................................................................................................ 39 4.5. Operational and environmental conditions................................................................................ 40 4.6. Material requirements................................................................................................................ 40 4.7. Vibration requirements.............................................................................................................. 40 4.8. Requirements for wiring and contact pins ................................................................................. 41 4.9. Electrical requirements.............................................................................................................. 42

5. Conclusions .......................................................................................................................................... 44 References .................................................................................................................................................... 45



List of Figures Figure 1. Approach for deriving MiniFaros laser scanner requirements.............................................. 7 Figure 2. Road safety evolution in EU, [7]. ............................................................................................ 12 Figure 3. Accidents distributed by car segments, [11]. ........................................................................ 13 Figure 4. Road fatalities by type of user in 2008, [7]. ........................................................................... 14 Figure 5. Distribution of fatalities by mode of transport, [8]. ................................................................ 15 Figure 6. Statistical sample of age, [11]. ................................................................................................ 16 Figure 7. Distribution of accidents by type, [11]. ................................................................................... 16 Figure 8. Accident types of serious or fatal accidents for heavy trucks, [12] and [13]. ................... 20 Figure 9. Initial speed of vehicles for pedestrian collisions, [11]......................................................... 25 Figure 10. Initial speed of vehicles for frontal collisions, [11].............................................................. 27 Figure 11. ADAS – user interest in targeted size vehicle segments [14]. ......................................... 30 Figure 12. Comparison of market price and customers ideas of price, [14]. .................................... 31

List of Tables Table 1: Advanced driver assistance functions, ADAS, overview. ..................................................... 10 Table 2: Accident types for cars. Possible driver assistance systems and potential for MiniFaros laser scanner. ............................................................................................................................................. 19 Table 3: Truck manoeuvre in accidents, [10] and [9]. .......................................................................... 21 Table 4: Accident types for trucks. Possible driver assistance systems and potential for MiniFaros laser scanner. ............................................................................................................................................. 22 Table 5. Distribution of pedestrian accidents, [11]................................................................................ 24 Table 6. Distribution of frontal crashes, [11]. ......................................................................................... 26 Table 7: Approximate sensor requirements for ADAS, [2]. ................................................................. 36 Table 8. Wide band random vibration profile values for body mounted parts. ................................. 41 Table 9. Connector wiring. ....................................................................................................................... 42



1. Introduction The project “Low cost miniature laser scanner for environmental perception”, MiniFaros, is a sensor development project aimed to significantly increase the penetration of advanced driver assistance systems, ADAS, on the automotive market. The project vision is to have a accident-free traffic environment by the use of effective environment perception systems. Laser scanners are the predominant generic environment sensing technology.

Poor human perception and assessment of traffic situations stands for the largest amount of traffic accident with fatal or severe injury outcome. Several safety functions are developed in order to prevent or mitigate many of these accidents. The system cost for these functions are often relatively high however so vehicles are rarely equipped with these systems, especially when it comes to smaller cars or commercial vehicles.

In order to develop the MiniFaros laser scanner it has to be defined how it should be used in order to set adequate requirements on it. This deliverable describes the relevant scenarios and operational requirements for the MiniFaros laser scanner for ADAS functions.

The sensor requirements will be derived from the top-down approach from accident scenarios to requirements according to Figure 1.

Figure 1. Approach for deriving MiniFaros laser sca nner requirements.

Accidentology

State-of-the-art ADAS functions

Object recognition requirements

MiniFaros laser scanner requirements

Relevant scenarios

User needs

Preliminary MiniFaros laser scanner performance



The first task will be to perform accident analysis and accident review in order to find the most relevant scenarios where accidents can be avoided or mitigated. Statistics from European countries and previous ADAS Integrated Projects have been studied. The result for this task is presented in Chapter 2 “Accident review and relevant scenarios”. These general statistics can help us to select the most useful functions to reduce accidents to be covered by the MiniFaros laser scanner.

There will be no safety function development in this project but the MiniFaros laser scanner will be used with current state-of-the-art functions developed in previous ADAS projects. These functions will be summarised in the next subsection briefly.

User needs on the sensor system is described in Chapter 3 “User needs”. Here are mainly the end user needs for an increased use of ADAS functions reviewed.

Automotive industrial user needs and sensor requirements are described in chapter 4. They are derived from the relevant scenarios, their safety functions and the user needs described in chapter 2 and 3.

In order to increase market penetration especially in the class of small vehicles, the market price has to be very low. The laser scanner is also developed to be small and lightweight in order to enable an easy integration in the vehicle design. The aim with the MiniFaros project is to develop a laser scanner that in series production will be relatively cheap to comparable sensors with other technologies such as radars.

To reach the objectives of small and low cost laser scanner a number of new techniques have to be developed and evaluated.

The rotating mirror in laser scanners has previously been realized with a macro mechanical scanning system. This relatively large moving part in the sensor has not been fully accepted by OEMs even though it has been proved reliable. By the use of a MEMS (Micro-Electro-Mechanical system) mirror in the novel laser scanner might alter this. By developing a MEMS mirror for the laser scanner will also enable downsizing of the sensor.

The receiver and the Time-to-Digital-Converter (TDC) are the major integrated circuits in the sensor. These components are essential when reducing the size and cost of the sensor. By integrating these into a common circuit will be an advantage regarding cost and size. The integration will also enable other benefits like compensation of timing error and a possibility to measure several pulses in a single event that will be beneficial for operation in bad weather conditions like rain and fog.

The use of free-formed optics and aspheric surfaces developed for the laser scanner will reduce the number of optical components and also make it possible to integrate the optics and the mechanics in the sensor.



This will also make it possible to decrease the sensor size and lower production cost further.

1.1. Advanced driver assistance systems overview This project’s goal is to develop a laser scanner able to serve several ADAS functions. The driver assistance systems already on the market or at research level are described shortly in Table 1. The functions are developed by previous European thematic network ADASE, Advanced Driver Assistance Systems in Europe, [1] and Integrated Project PReVENT [2] with sub-projects. These systems are using various sensors to monitor traffic environment. Often multiple sensors are used and their data is fused on a higher or lower level.



Table 1: Advanced driver assistance functions, ADAS , overview.

Function acronym Meaning of acronym Short system description

CAS Collision Avoidance Systems

FCW Forward Collision Warning

Warn for obstacles including other vehicles in front of the host vehicle.

Pedestrian protection Warn and act if pedestrians are in front of

the host vehicle.

LCW Lateral Collision Warning

Warn for vehicles coming from the side of the host vehicle and crossing the path.

LDW Lane Departure Warning

Warn if the host vehicle is about to unintentionally exit the current lane or road.

LKA Lane Keeping Assistant

Functionality like the LDW but with ability to actively steer back.

LCA Lane Change Assistant

Warn if the adjacent lane is occupied if a lane change manoeuvre is initialised.

Start inhibit Inhibit host vehicle (truck) to start when an object is in the blind spot in front.

Intersection assistance

Monitor and warn for oncoming traffic in intersection scenarios.

CMS Collision Mitigation Systems

AEB Automatic Emergency Braking

System that automatically start to brake if a frontal collision is unavoidable.

Pre-crash

System that prepare passive safety systems like airbag, seatbelt pre-tensions, active hoods for pedestrian protection, etc, when a collision is unavoidable.

DAS Driver Assistance & Comfort Systems

ACC Adaptive Cruise Control

System that automatically keeps a safe distance to the vehicle in front.

S&G Stop and Go assistance

Like ACC but for lower speeds and ability to stop host vehicle and automatically start again while driving in queue.

Parking assistant Monitoring objects close to host vehicle in

parking situations.



2. Accident review and relevant scenarios Initially an accident analysis has been performed to survey the importance of the various scenarios that will be relevant for the MiniFaros laser scanner as well as the importance of other parameters, in order to derive the selection of use cases. A number of data sources have thoroughly been investigated leading to frequency percentages per accident type. Finally, scenarios relevant to MiniFaros laser scanner have been identified and scenarios addressed for further development of the sensor have been described.

2.1. Databases investigated The sources investigated for the accident analysis are the following:

• Statistic results from literature for the following two European databases :

• Community Road Accident Database, CARE, [7], European Commission, 2006

• Annual Reported statistics for Road Casualties in Great Britain, [8], 2008

• Results from the Integrated European Project PReVENT, [2]

• National Highway Traffic Safety Administration, NHTSA, General Estimates System, GES [9], 2008

• Fatality Analysis Reporting System, FARS [10], 2008

• German In-Depth Accident Survey, GIDAS [11]

2.2. Main results of accident review Looking at the accidental data in Europe in the last two decades the overall number of road accidents involving personal injuries has only decreased slightly. However the number of road fatalities was reduced by almost 50% as shown in Figure 2.



Figure 2. Road safety evolution in EU, [7]. Additionally the “injured” and “accidents” curves displayed in Figure 2 have almost the same shape. These facts can be interpreted as even though the traffic in terms of fatal accidents improved significantly, the traffic facilities are still not safe enough to increase injuries prevention.

Not to be mistaken, however, the number of 38 000 of deaths in 2008 is still too many to be satisfied with. More precautions are to be taken by all the traffic stakeholders in Europe.

The economical impact is also not to be underestimated. For example only in Czech Republic the total costs of accidents has almost achieved 306 billion € in 2008, which was approximately 11% of the republic’s annual budgetary deficit.

It is important to realise that considering the growth of quantity of cars in Europe with over 40% since 1990 it would be false to declare that traffic safety is not improving. In terms of life saving and injury prevention, the steps taken in Europe, with e.g. mandatory usage of systems like ESP or passive pedestrian protection, are definitely helping. But there is a belief that the safety can be improved even more radically by increasing the ADAS penetration in European automotive market. For this we need cheap sensor systems.



2.2.1. Accidents by car segments

The need of developing cheap sensors suitable for small cars is displayed in Figure 3. The figure shows in what segment the car, which belongs to the driver responsible for an accident, fall in. Notice that 56% of all accidents are caused by cars belonging to segments A, B and C. These segments are unfortunately literally untouched by advanced driver assistance and active safety systems today. Even the penetration of D segment by these systems is very low.

Figure 3. Accidents distributed by car segments, [1 1]. Examples of cars in the different segments in Figure 3 are.

• Subcompact: Opel Corsa, VW Polo, Ford Fiesta, Skoda Fabia, Audi A2, Fiat Punto, Ford Fusion, Honda Jazz

• Compact: VW Golf, Opel Astra, Ford Focus, Mercedes A-Klasse, Audi A3, Skoda Octavia, Toyota Corolla

• Mid size: Audi A4, VW Passat, Opel Signum, Ford Mondeo, Honda Accord, Mazda 6, Škoda Superb, Volvo S40/S60

• Full size: Audi A6, Mercedes E-Klasse, Volvo V70, Jaguar S-Type, Mercedes CLK, Peugeot 607, Saab 9-5

• Full size luxury: Audi A8, BMW 7er, Mercedes S-Klasse / CLS, VW Phaeton

• Sport cars: BMW 6 Series, Mercedes CLK, Volvo C70, Volkswagen Eos, Audi TT, BMW Z4, Porsche Boxster/911



2.2.2. Accidents by road users

The distribution of fatalities among the various road users is depicted in Figure 4. Drivers constitute the highest number of fatalities with about 60%, followed by vulnerable road users, VRU, like pedestrians, 20%, passengers 19% and others/not specified 1%.

Figure 4. Road fatalities by type of user in 2008, [7]. According to the annual statistical report from Great Britain, [8], the percentage of passenger car driver fatalities are 53% and pedestrians 19%, see Figure 5. Car occupants and pedestrians account for the vast majority of road fatalities.



Figure 5. Distribution of fatalities by mode of tra nsport, [8].

2.3. Accident review for cars For a more comprehensive accident analysis and scenario selection for passenger car more than general statistical data is required. For this purpose the data obtained from GIDAS, [11], is used.

2.3.1. Used statistical sample

The selected statistical sample of the GIDAS, [11], database in this survey is:

• Accidents recorded between 07/1999 and 02/2010

• Accidents between passenger car and any other traffic participant

• No selection on severity set for accidents with injuries of any kinds

• Accidents caused by breaking the law (e.g. traffic light violation) is included

The statistical sample consists of accidents with the attributes seen in Figure 6 below.



Figure 6. Statistical sample of age, [11].

2.3.2. Types of accidents for cars

The accident data obtained from [11] allows categorization of accidents into the following basic groups shown in Figure 7.

Figure 7. Distribution of accidents by type, [11].



1. Start, stop: Collision with another vehicle which starts stops or is stationary. Starting or stopping are here to be seen in connection with a deliberate stopover which is not caused by the traffic situation. Stationary vehicles within the meaning of this kind of accident are vehicles which stop or park at the edge of a carriageway, on shoulders, on marked parking places directly at the edge of a carriageway, on footpaths or parking sites. The traffic to or from parking spaces with a separate driveway belongs to No. 5 kind of accidents.

2. Drive ahead, wait: Collision with another vehicle moving ahead or waiting. Accidents caused by a rear-end collision with a vehicle which either was still moving or stopping due to the traffic situation. Rear-end collisions with starting or stopping vehicles belong to the No. 1 kind of accidents.

3. Lateral, same direction: Collision with another vehicle moving laterally in the same direction. Accidents occurring when driving side by side (sideswipe) or when changing lanes (cutting in on someone).

4. Drive towards: Collision with another oncoming vehicle. Collisions with oncoming traffic, none of the colliding partners having had the intention to turn and cross over the opposite lane.

5. Incurve, cross: Collision with another vehicle which turns into or crosses a road. This kind of accident includes collisions with crossing vehicles and with vehicles which are about to enter or leave from/to other roads, paths or premises. A rear-end collision with vehicles waiting to turn belongs to the No. 2 kind of accidents.

6. Vehicle-pedestrian: Collision between vehicle and pedestrian. Persons who work on the carriageway or still are in close connection with a vehicle, such as road workers, police officers directing the traffic, or vehicle occupants who got out of a broken down car are not considered to be pedestrians. Collisions with these persons are recorded under the No. 10 kind of accidents.

7. Obstacle at roadbed: Collision with an obstacle in the carriageway. These obstacles include for instance fallen trees, stones, lost freight as well as unleashed animals or game. Collisions with leashed animals or riders belong to the No. 10 kind of accidents.

8. Run off to the right

9. Run off to the left: Leaving the carriageway to the right or left. These kinds of accidents do not involve a collision with other road users. There may however be further parties involved in the accident, e.g. when the vehicle involved in the accident veered off the road trying to avoid another road user and did not hit him.



10. Other traffic accidents: Accident of another kind. This category covers all accidents which cannot be allocated to one of the kinds of accidents listed under Nos. 1 to 9.

2.3.3. Addressed accident types by MiniFaros laser scanner for cars

Figure 7 shows that there can be four categories of accidents identified which represent the most of the accidents.

Frontal crash related accidents can be considered types 1, 2 and 4 with a total share of 32%. These accidents can be avoided or at least partially mitigated by a pre-crash system. Therefore pre-crash is selected as one of the systems determined to be developed for demonstrator passenger car.

The second group of accidents is related to leaving or more precisely running off the roadbed. These are the categories 8 and 9 with a total share of total 24% of the accidents. To handle these types of accidents, functions similar to lane departure warning, LDW, are required. The MiniFaros laser scanner will not be able to be used for lane monitoring. These types of accidents are therefore beyond the scope of this project.

The third group is somehow related to intersections and junctions. To this group belongs the category 5 of accidents displayed in Figure 7 with approximately 20% of the accidents.

The fourth group of accidents is with pedestrians. With a total percentage of 18% of all accidents, the need of using pedestrian protection system for the demonstrator passenger car is obvious.

The scenario types in Figure 7 can be summarised in Table 2 where also possible assistance systems for the accidents are displayed. In the table potential for the MiniFaros laser scanner in relevant assistance systems are also shown based on preliminary performance. In total about 54%-74% of the accidents can be addressed with an ADAS function that get data from a MiniFaros laser scanner.



Table 2: Accident types for cars. Possible driver a ssistance systems and potential for MiniFaros laser scanner.

Accident type Description Frequency Possible ADAS

function

MiniFaros laser

scanner potential

1 Longitudinal collision 3% FCW, Pre-crash,

AEB Yes

2 Longitudinal collision

19% FCW, Pre-crash, AEB, ACC, S&G

Yes

3 Lateral collision

3% LCW, LCA Yes

4 Longitudinal collision

10% FCW, Pre-crash, AEB

Yes

5 Intersection collision

20% Intersection assist

Yes for urban situations

6 Pedestrian collision

18% Pedestrian protection

Yes


AEB Yes

8 Lane departure 14% LDW, LKA No

9 Lane departure 10% LDW, LKA No

10 Misc. collisions 2% - No

100% 54%-74%

2.4. Accident review for trucks In this paragraph there will be a summary of the data found in different databases, regarding accidents involving heavy trucks.

2.4.1. Types of accidents for trucks

In order to find relevant scenarios for the MiniFaros laser scanner a more detailed analysis of accidents with heavy vehicles have to be evaluated. Analysis from [12] and investigations by [13] has been used for this review. Figure 8 shows the distribution of different accident types from these studies.



Figure 8. Accident types of serious or fatal accide nts for heavy trucks, [12] and [13]. The types of accident shown in Figure 8 can be described as:

1. Single, run off. This accident type is the type where most heavy vehicle users are killed or seriously injured. The accident is often in rural areas or highway approaches.

2. Drive ahead, wait: Collision with another vehicle moving ahead or waiting in the same lane.

3. Drive towards. Collision with an oncoming vehicle.

4. Incurve, cross. Collision with another vehicle which turns into or crosses the road. Intersection accidents.

5. Lateral, same direction. Collision with another vehicle during lane changing manoeuvres or while overtaking or being overtaken.

6. Vulnerable road users. Collision between vehicle and pedestrian, cyclist, motorcyclist or other vulnerable road user.

7. Other accidents. These accidents are other not suitable in the above types. It could for example be other vehicles running into the heavy truck rear end while stationary.

Statistics from [9] and [10] shows that large trucks accounted for 8% of the vehicles in fatal crashes, but only 2% of the vehicles involved in injury crashes and 4% of the vehicles involved in property-damage-only crashes. Of the 4,066 large trucks involved in fatal crashes, 74% were combination trucks.



Vehicle manoeuvres of trucks involved in an accident are shown in Table 3. In a majority of accidents the truck is going straight when the collision occur indicating head on collisions. This manoeuvre involves then accidents with vulnerable road users. The second largest is the single accident type when a curve is negotiated and control over vehicle is lost. After that vehicle stopping in lane and intersection manoeuvres while making a left turn is the most common.

Table 3: Truck manoeuvre in accidents, [10] and [9] .

Vehicle manoeuvre Share of fatal accidents, [10]

Share of all accidents, [9]

Going Straight 69.5% 53.2%

Decelerating in Traffic Lane 2.8% 5.3% Accelerating in traffic lane 0% 0.2%

Starting in Traffic Lane 0.8% 2.0% Stopped in Traffic Lane 5.7% 11.9%

Passing or Overtaking Another Vehicle 1.1% 0.9%

Disabled or Parked in Travel Lane 0.02% 0.14%

Leaving a Parked Position 0.07% 0.3% Entering a Parked Position 0.05% 0.1%

Turning Right 1.8% 3.3%

Turning Left 4.5% 11.2% Making U-Turn 0.4% 0.5%

Backing up (not parking) 0.9% 0.7% Changing Lanes or Merging 1.7% 3.2%

Negotiate a Curve 8.2% 5.0%

Other/unknown 0.8% 1.9%

2.4.2. Addressed accident types by MiniFaros laser scanner for trucks

The heavy truck accident analysis shows that there are three categories of accidents that represent the most of the accidents as shown in Figure 8 in section 2.4.1.

Front crash related accidents are the major accident types and covering in total 37% of all serious or fatal accidents. That is Type 2 and 3 in Figure 8. To decrease these accidents or to decrease the severity of the accidents forward collision warning systems, FCW, automatic emergency braking, AEB, or other pre-crash functions could be of use. The pre-crash functions could be preparing the vehicle for a crash like deploying airbags,



pretension of seatbelts when the accident is unavoidable. Functions for this are developed in [4]. Other functions that could be of help are adaptive cruise control, ACC, and Stop and Go, S&G. All these functions could be a possible for the MiniFaros laser scanner.

A second accident type is the collision with vulnerable road users, VRU, with 24% of the accidents. These accidents often occur due to a limited vision for the driver due to large blind spot areas of a truck. By monitoring the environment close to the truck by sensors the number of this type of accidents could be decreased. Functions for VRU monitoring and warnings like blind spot detection could also be possible for the MiniFaros laser scanner.

The third main accident type is conflicts at intersections and crossing traffic with 18% of the accidents.

The scenario types in Figure 8 are summarised in Table 4 where also possible assistance systems for the accidents are displayed. In the table the MiniFaros laser scanner potential for relevant ADAS functions are also shown based on preliminary performance of the MiniFaros laser scanner. In total about 64% to 82% of the accidents can be addressed with an assistance system that might incorporate a MiniFaros laser scanner.

Table 4: Accident types for trucks. Possible driver assistance systems and potential for MiniFaros laser scanner.

Accident type Description Frequency Possible ADAS

function

MiniFaros laser

scanner potential

1 Lane

departure, roll-over

5% LDW, LKA No


AEB, ACC, S&G Yes


AEB Yes

4 Intersection collision

18% Intersection assist

Yes for urban situations

5 Lateral collision

3% LCW, LCA Yes

6 VRU

collision, 24% Pedestrian

protection, FCW, Pre-crash, AEB

Yes

7 Misc. collisions

13% - No

100% 64%-82%



2.5. Accident review for pedestrians As shown in the previous chapter pedestrians are an exposed road user for accidents in Europe. In this paragraph a summary of the outcomes elaborated from different database investigations on accidents with pedestrians is presented.

Pedestrian and other vulnerable road user fatalities are more common within urban areas than outside since the pedestrian density is higher in cities than outside.

2.5.1. Accident type for pedestrians

The most common collision opponents for pedestrians are passenger cars. Indeed 77% of pedestrian fatalities in 2008 in Great Britain, [8], are related to passenger cars as collision opponent.

An analysis in APALACI, [4], a subproject of the PReVENT IP, [2], proved that concerning the pedestrian movement, 65% of involved pedestrians have crossed the road from the right to the left, for right hand traffic. About 60% of the pedestrians were walking and 20% were running at the time of collision. The remaining 20% were either stationary or moving in some other way. Other results from [2] show that about 70% of the pedestrian collisions the impact occurs at the front end of the vehicle. The point of impact in the front region is rather evenly distributed between front 40%, right 35% and left side 25%.

According to GIDAS, [11], 97% of accidents with pedestrians occur in urban areas and three main groups of pedestrian accidents can be identified as depicted in Table 5.



Table 5. Distribution of pedestrian accidents, [11] . Ratio to

pedestrian accidents Accident type Case description

Pictogram examples

[11] All Severe or fatal

I. Case vehicle turning,

pedestrian crossing road

Turn left or right, pedestrian coming either from left or right, including

reduced visibility cases

7.5% 4.5%

II. Case vehicle driving straight,

pedestrian crossing road

a. Pedestrian crossing road from

the left or right, visibility was not

reduced

55.4% 79.8%

b. Pedestrian crossing road from

the left or right, visibility was

reduced

24.8% 9.0%

III. Case vehicle driving straight, pedestrian not crossing road

Pedestrian moving longitudinally, same or opposite direction

to CV

2.6% 6.7%

IV. Special cases

e.g. pedestrian suddenly “appears” on the roadbed or is

seen in the last moment

1.7% 0%

92% 100% As can be seen in Figure 9 that is linked to Table 5 the Type I of the accidents happen at lower initial speeds. In fact 80% of all accidents take place up to a case vehicle speed of 30km/h.



Figure 9. Initial speed of vehicles for pedestrian collisions, [11].

2.5.2. Time of day and road conditions

Regarding environmental factors the investigation in [2] and subproject [4] identified that most of the accidents, 68%, happened during daylight hours. A large portion of the remainder, 17%, occurred on unlit roads at night and further 8% happened on dark roads with streetlights. Visibility was reduced by fog, mist, or heavy rain in 7% of the cases. 67% of the accidents occurred on dry roads, 31% on wet roads, and 2% on icy, slippery, or snow covered roads.

2.5.3. Addressed accident types by MiniFaros laser scanner for pedestrians

The accident analysis of pedestrian summarised in Table 5 shows that covering accident when pedestrians cross the road and the vehicle is either is driving straight or is making a turn (types I and II) would prevent or mitigate consequences of 88% of pedestrian related accidents. It is difficult to cover all cases however. For example it may prove impossible to prevent accident where the pedestrian suddenly appears on the road. Not only to mention accident type IV, but also Type IIb, where the visibility is reduced by an obstacle. Nevertheless, even when not considering these scenarios, more than 65% of pedestrian accidents could be covered.



2.6. Accident review for frontal crash The other major accident type is frontal crash and it is studied more in detail in this section. The majority of all accidents happen with at least one vehicle hitting frontally. This vehicle is considered to be the case vehicle, CV. The selected scenarios describe the most frequent types of frontal crash accidents. Accidents happening at junctions when one vehicle is turning and crashes into second vehicle driving straight are not considered here. The distribution of different accident types for frontal crash is shown in Table 6.

Table 6. Distribution of frontal crashes, [11]. Ratio to frontal

accidents Accident type Case description

Pictogram examples

[11] All severe or fatal

I. CV hits frontally to another vehicle

Initial speed before the crash is a sum of both vehicles’ initial speeds.

22.6% 68.4%

II. CV driving straight, another vehicle changing to same lane or is already moving in the same lane

Initial speed before the crash is

difference of both vehicles’ speeds

33.0% 23.7%

III. CV driving straight, another vehicle is moving with minimal longitudinal speed (e.g. turning, driving out of parking place etc.)

Initial speed is practically speed of

the CV

39.2% 5.3%

IV. CV hits into solid obstacle

5.1% 2.6%

99.9% 100%



The initial speed of the case vehicle, CV, in the accident types in Table 6 are shown in Figure 10.

Figure 10. Initial speed of vehicles for frontal co llisions, [11].

As can be seen in Figure 10, over 80% of accidents happen in range up to 40 km/h. Type II of accidents is the exception. In this type 70% of accidents happen in this range. It’s important to realize that in Type I and Type II of accidents the initial speed of CV is not the actual crash speed. In Type I when two cars are hitting frontally, the crash speed is a sum of both vehicles’ speeds. In Type II it is on the contrary the difference of these speeds. Actual relative speeds of accidents are not available and depend on how much the drivers are able to brake prior to the impact.

2.6.1. Addressed accident types by MiniFaros laser scanner for frontal crashes

Accident types for frontal crashes have been discussed in the accident reviews for cars and trucks. About a third of all severe accidents are related to frontal crash types. Around 80% of the accidents are at speeds in the range of 0-50 km/h according to Figure 10. The most severe accidents are head to head according to Table 6, Type I. This accident type is to be addressed by the MiniFaros laser scanner with a Pre-crash or FCW functionality. Type II in Table 6 is also addressed by these functionalities. Other functions to keep a safe distance or S&G or ACC are also addressing this accident type.



3. User needs Considering the user needs on the MiniFaros laser scanner, several needs can be addressed. These will be allocated into two basic user areas and described in following parts.

Needs of the road user – this is the first thing needed to be taken into account. Here, the road user is actually the customer, who will purchase the system at the end. Its demands are mainly oriented on price to function ratio.

The needs from the industrial user – the developed sensor has to fulfil certain characteristics to be easily adopted into automotive sector. These characteristics include electrical and mechanical properties, which will be described in the sensor requirement in Chapter 4.

3.1. Requirements from the road user One of the main purposes of the newly developed sensor is to increase the volumes of cars equipped with advanced driver assistance systems, ADAS in order to significantly reduce the number of accidents on the roads of Europe. This goal is only achievable through finding an optimal ratio between the customer’s satisfaction and producer’s business intention.

To find such a ratio is quite difficult however, especially in low class and mid class vehicle segments. According to AUTOTECH, survey in 2006, [14], which was done in scope of 40.000 people from United Kingdom, Germany, Italy, France and Spain, there are significant differences between what owners of different vehicles think to be a reasonable price to even consider the purchase of ADAS. To correctly address the best ADAS functions, which are to be focused on, confronting the market analysis with the accident statistics is highly contributory.

3.1.1. Demographical data for survey on ADAS

Detailed demographical data are to be found in [14]. Only overall basic data shall be described here:

In the survey 57% were men and 43% women.

65% of respondents were married or living with partner, 25% were singles and never married. Remaining percentage of respondents was in other marital status.

9% of respondents were in age of 18-24, 22% of respondents were in age of 25-34, 23% of respondents were in age of 35-44, 22% of respondents were in age of 45-54 and 21% of respondents were in age of 55+.

68% of respondents didn’t have children in household.



3.1.2. Results from road user survey on ADAS

The following figures demonstrate how people did react on presented ADAS functions before and after the actual market price was introduced to them. The actual market price in 2006 and the price considered by the users as a “good value” is part of the Figure 12.

From above mentioned figures and by considering only the functions suitable for laser scanner, several conclusions can be made. For instance, let’s have a closer look to ACC system. According to Figure 11, there was a great initial interest in ACC technology even in the segment of small size vehicles. Unfortunately, after the market price was presented to the persons questioned, they initial interest of 22% dropped to 11%. Figure 12 shows the reason for it. The “good value” estimated by the potential customers not even reached the half of the market price. Meaning that for small size vehicle market penetration of 22% by the ACC system, the market price would have to drop approximately to 500€.

Unfortunately, this would not even cover the car producer’s expenses for only buying and implementing the required sensor technology into vehicles in 2006. It is also needed to be noted, that the “good value” price in Figure 12 was estimated by the examined, who were willing to purchase the system even before the market price was presented to them. According to results of the AUTOTECH survey, the “good value” price estimated by the questioned persons, who were not willing to purchase the system before the market price introduction is for about 100€ lower. This makes the attempts to penetrate small size vehicle segment by ACC system even more difficult.



Figure 11. ADAS – user interest in targeted size ve hicle segments [14]. Attempting to identify the best ADAS functions suitable for a cheap laser scanner, two of them is showing a greater potential than the others. According to Figure 11, the initial value of pedestrian protection system and pre-crash/radar enabled collision warning system increased even after the market price was introduced to questioned persons. With the cheap technology of environment sensing, the estimated market price addressed in AUTOTECH survey could be achievable and thus it seems that the customer’s satisfaction and producer’s business intention would meet in these two functions.

Pedestrian protection system and pre-crash system were also identified in chapter 2.3.2. The third ADAS function for laser scanner is to be defined for passenger car and truck demonstrator. ACC system is not to be considered for its technical complexity and wide connections to another car systems not usually included in small size cars. For lane departure aid, video camera might be needed which makes the system also not considerable. As parking aid is not primarily an active safety system, the only left to consider is blind spot detection & warning. Even though this



would be a reasonable choice with respect to accident statistics, two other sensors might be needed for the function to integrate (driver and passenger sides). Because we have already selected two systems which will use only one laser scanner mounted in front of the vehicle, it speaks for itself to integrate another function requiring only one front laser sensor. The example of such system is turning assistance already described in the INTERSAFE project, [5]. The other option is warning against not keeping the minimum recommended distance between vehicles. Regarding police records of Czech Republic 22.7% of all accidents were caused because of this reason.

Figure 12. Comparison of market price and customers ideas of price, [14]. Note: There is a slight pricing inaccuracy in the survey of the blind spot detection & warning system and lane departure warning and aid (both marked with * in the Figure 12). Even in 2006 the price of these systems did not reach the value presented by the survey (900€ and 800€ respectively). The charts already calculate with corrected prices of 550€ and 500€ respectively, which were the common prices in 2006 in luxury vehicle segment.



4. Sensor requirements The MiniFaros laser scanner needs to be prepared for use in automotive industry. Fulfilling the basic requirements described in the following chapters should prepare it for easier integration and use in possible series production later.

4.1. Functional requirements The ADAS functions described briefly in Table 1 and referenced to in the accident review and relevant scenarios are using various sensors to monitor traffic environment. Often multiple sensors are used.

No new ADAS functions will be developed in this project specific for the MiniFaros laser scanner. A survey on the approximate sensor requirements for the different state-of-the-art ADAS functions and the sensor technology used today, in production or in research projects are shown in Table 7. The requirements are mainly from the PReVENT [2] sub-projects, [3], [4], [5] and [6]. In the table there is also a column for the MiniFaros laser scanner potential to be used based on preliminary theoretical sensor performance stated in the MiniFaros Technical Annex.

4.1.1. Sensor output requirements

The sensor output known as sensor observation should include the following.

• Object track parameters such as: o ID: Provides a unique identification name for every physical

property which was measured by the sensor and is of our interest. The maintenance or on time update of the IDs during the tracking process is important for the successful sense of the surrounded environment.

o Position: This is the spatial location of the measured physical property.

o Measurement: This is the value of the physical property as measured by the sensor element.

o Confidence: This is a generic term referring to many different types of errors in measurement such as measurement errors, calibration errors or environmental errors. The errors that are not defined in sensor data sheet may be calculated internally for measurement validation by the sensor.

o Time stamp: This is the time when the physical property was measured. In real time systems, the time of a measurement is often as important as the value itself.

o Velocity: This is the estimated velocity of the physical property that is calculated by object tracking.

o Tracking life time: This is the time a tracked object is maintained in successive scans.



o Hidden status: Value that indicates whether the track has been associated with a measurement in the current scan and equals to zero or if it is just an estimation based on previous estimation.

• Object represented by contour. Contour-based object tracking requires object detection only once.

• Object classification. The recognized objects should be classified as:

o Pedestrian o Car o Track o Motorcycle o Cyclist o Unclassified ( for other objects such as stationary objects)

4.1.2. Processing output requirements

Correct output from the processing is fundamental for a successful object detection and tracking. It must implement and fulfil the following requirements:

• ID maintenance: It is important to maintain the same ID for a physical property scanned by the laser during the process. Shape changes of a detected object must be overcome.

• Track splitting and merging: It is important to detect when tracks merge or split because it is a common reason for "ghost" track appearance or track disappearance respectively. If a track splits, then there will be two segments overlapping the track and when two tracks merge, then there will be two tracks overlapping one segment.

• Real or near-real time data distribution: In a safety application as applied in MiniFaros, the fast processing and delivery of information is just as important as the information itself. So during the design and implementation, issues regarding data availability should be of the highest priority. As a result, all the information provided to the perception modules should be transmitted with the lowest latency possible. With the term “near real time” it is assumed that the delay in information distribution is acceptable as long as it stays under a timing threshold which is defined by the application requirements and the algorithms capabilities.

• Update frequency: The update frequency is the frequency of which the tracking of an object should be updated. Sensors for detection of vehicles, VRUs and roadside objects shall have a detection rate of at least 5 Hz. Specific update frequencies are specified in Table 7 for different ADAS functions.



• False alarm: Detecting moving objects in the vehicle environment and tracking them helps to recognize false alarms and reduce unwanted detections.

• Accuracy and resolution: It is the difference between the observed and real value. It needs to be at an appropriate level for the required application task.

• Compensation: This refers to the ability of a sensor to detect and respond to changes in the environment through self-diagnostic tests, self-calibration and adapt adaptation.

• Information processing: This refers to processes such as signal conditioning, data reduction, event detection and decision making, which enhance the information content of the raw sensor measurements.

• Communications: This refers to the use of a standardized interface and a standardized communication protocol for the transmission of information between the sensor and the outside world.

• Compensating of misalignments, bandwidth limitations and data latencies: In a real time application the perception output should be able to deal with temporal bandwidth restrictions, delays on the communication channel, misalignment of sensors and malfunction or loss of one or more sensors. In addition, the system should be able to identify situations and give the necessary warnings when the provided output is of low reliability due to problems in the data acquisition process.

4.1.3. Range requirements - object recognition

The requirement of the laser scanner range is divided into three parts with descending distance from the sensor:

• Object detection range: Distance where an object can be detected. This is not a requirement from a functional point of view but have to secure a correct object tracking.

• Object tracking range: Distance where an object can be tracked which requires several object detections in order to get information of the objects position and speed. This is an ADAS function requirement found in Table 7.

• Object classification range: Distance where the object has to be classified into different categories in order to support the correct decision for the ADAS function. Classification range is also an ADAS function requirement found in Table 7.



4.1.4. Range accuracy

In order to determine the position and speed accurately enough the range accuracy is specified in Table 7 for the different ADAS functions

4.1.5. Field of view

The field of view requirement is based on where the ADAS functions have to monitor the environment. Multiple sensors might be needed if single sensor locations are hard to find on the vehicle. The aim is to use few sensors in order to reduce costs.



Table 7: Approximate sensor requirements for ADAS, [2].

Min

iFar

os

lase

r sc

anne

r po

tent

ial

Yes

Yes

Yes

No

Yes

No

Yes

Yes

No

Yes

Yes

Sen

sor

tech

nolo

gy

toda

y

Rad

ar,

cam

era,

lase

r

Cam

era,

lase

r

Rad

ar,

cam

era

Cam

era

Cam

era

Lase

r,

Cam

era,

rad

ar

Lase

r,

Cam

era,

rad

ar

Rad

ar

Rad

ar

Cam

era,

la

ser,

cam

era

Ultr

ason

ic,

cam

era

Upd

ate

freq

uenc

y [H

z]

25

12,5

12,5

25

<12

,5

12,5

12,5

>25

<12

,5

<12

,5

<12

,5

Ang

ular

A

ccur

acy

[deg

]

Up

to 0

,25

0,25

1 0,1 2

Up

to 0

,25

Up

to 0

,25

1

Up

to 0

,3

1 2

Fie

ld o

f vi

ew

[deg

]

30

60

100

50

Up

to

180

250

250

60

(veh

icle

w

idth

)

10-2

0

Up

to

180

Up

to

180

Ran

ge

accu

racy

[m

]

0,5

0,1

0,2

0,2

0,2

0,3

0,3

0,1

0,3

0,1

0,1

Cla

ssifi

catio

n

25-5

0

30

(15)

- (5)

Up

to 5

0

Up

to 2

0

-

Up

to 2

0

Up

to 2

0

-

Ran

ge [m

]

Tra

ckin

g

50-8

0

40

30

100 5

Up

to 1

90

Up

to 8

0

20

Up

to 2

00

50

5

Ass

ista

nce

syst

ems

FC

W, A

EB

[3

], [4

]

Ped

estr

ian

prot

ectio

n

LCW

, LC

A

[6]

LDW

, LK

A

Sta

rt in

hibi

t [4

]

Inte

rsec

tion

assi

stan

ce -

ru

ral [

5]

Inte

rsec

tion

assi

stan

ce -

ur

ban

[5]

Pre

-cra

sh

[4]

AC

C

S

&G

Par

king

as

sist

ant



4.2. Functional requirements for Infrastructure las er scanner

INTERSAFE-2 performed a requirement analysis for infrastructure sensors for intersections, which are the predominant areas to be observed by such sensor systems [16]. The information gained might be sent to the surrounding vehicles via V2I communication to enable a warning of the driver or to display warnings at the roadside.

Infrastructure-based sensors shall be designed to deliver information from selected directions, lanes or corners. As the positioning of the sensors will remain fixed throughout the entire life time of the system, each particular case needs careful consideration in advance. Such cases comprise detecting vehicles entering and exiting an intersection, VRU’s, other dynamical-nature roadside objects possibly posing a threat for intersection users.

The infrastructure will pre-process the data before transmitting it to the vehicle. The pre-processing means filtering and possible fusion for increasing robustness and reliability of the object detection. Moreover, the system removes redundant data and transmits to the vehicle system only unique object detection results.

4.2.1. Object detections and measurement

The system must be able to detect vehicles (cars, motorbikes, trucks) entering and exiting an intersection. The location of each vehicle is expressed in a sensor-specific coordinate system using distance and direction. Moreover, the speed of each vehicle will either be estimated by differentiating consecutive location measurements or directly, in case the sensor supports such a function. An important quality of the detection system is that it can also estimate the total length of vehicles stopped or currently stopping in e.g. an intersection, as such information can be utilised for estimating free space in front of the vehicles still entering the intersection.

Specific sensors will be assigned for detecting VRU’s (pedestrians and cyclists) and similar-sized roadside objects that clearly differ from static objects. Just like vehicles, these objects will be measured for their location (distance, direction) and speed.

4.2.2. Estimation of Needed Coverage Area

For vehicle existence, location, size and speed detection the longitudinal range of the coverage area depends on the speed range used for the lane or direction the sensing system covers. Moreover, the longitudinal range is dependent upon the maximum number of vehicles stopped and waiting for entering e.g. an intersection. The coverage area should start from the stop line at the intersection. This implies longitudinal detection ranges of



50 m – 100 m for 50 km/h host vehicle speed

as listed in INTERSAFE-2 deliverable (table 6,7,8,9) [16], depending on the application.

Additional sensors can be used for sensing the intersection itself (at least for detecting obstruction existence in the intersection area). Each sensor should cover at least one lane or preferably more at a time.

For VRU detection and location or speed measurement the longitudinal detection range should be high enough to enable detection, localisation, and speed measurements of fast-moving bicycles with a suitable detection and reaction margin. If location differentiating is used for measuring speed, in addition to the margins the detection range should cover at least three measurement cycles. Assuming 40 km/h maximum speed for bicycles the range should be max. 50 m.

4.2.3. Data acquisition rate

For vehicle and VRU/ roadside object detection the detection rate should be at least 5 Hz. This is particularly the case if vehicle speeds are measured by using differentiation.

4.2.4. Practical Limitations

All detection systems should be small enough so that they can be installed either directly above, aligned or perpendicularly above their coverage areas.

Housings have to withstand all normal outside weather conditions throughout the year, -40°C to +50°C. The sensing system should be able to operate also without specific maintenance like cleaning.

Measurement systems should use either 12 V/ DC or 230 V/ AC power supplies with normal industrial deviation ranges and over voltage/ inverse polarity protection.

The systems must have a self-diagnosis capability in order to inform an operator if some of the components are not working properly.

4.3. Non-functional requirements It is necessary that the processing should be decentralized (where this is feasible) where many software modules will be possible to be reused regarding the processing of sensor observations.

• Open-layered architecture: A layered design approach facilitates the system design by hierarchically partitioning a complex design problem. It applies the principle of “divide and conquer” to the systems engineering problem. With this approach, the implemented code and the role of each software module are consistent and clear and finally the errors are easily traceable during the debug and fine-



tuning phase. In addition, changes during the implementation phase are much more flexible.

• Software modularity: Modules that perform the same tasks don't have to be implemented more than once, so it is necessary to design modular and flexible software.

• Application independence: Processes like filtering, tracking and data-association should be reusable and be dependent only on the lower levels of processing hierarchy and should not be affected by the higher levels of application.

• Flexible reconfiguration - scalability: In case that more sensors are added to the system or higher level applications require more functionality, then this should mean little or no modifications on the existing code. In a full flexible and scalar system, such changes should only imply the development of new modules.

4.4. General requirements General sensor requirements are taken from [15]. These apply for all electronic devices being designed for automotive usage.

R1: The laser scanner shall be designed so that the manufacturing cost can be low for mass production. Project aims at a manufacturing cost of less than 40 € of the laser scanner. Not required for prototypes.

R2: Equipment dimensions shall be practical for the demonstrator vehicle to which they are to be fitted. Laser scanner dimensions shall be small. Project aims at 4 by 4 by 4 cm for production sensor. Maximum prototype size is however 120 mm by 80 mm by 80 mm.

R3: Equipment housings shall be designed or selected to fit with the demonstrator vehicle design.

R4: Vehicle-mounted equipment shall comply with relevant automotive standards and accepted practice. Not required for prototypes.

R5: Individual vehicle-mounted components should weigh no more than 1kg each.

R6: Vehicle-mounted equipment shall be robust to expected vibration (equivalent to the vehicle driving over a cobbled road surface).

R7: Vehicle-mounted equipment should incorporate mechanical shock detection.

R8: Cable length between sensors and external processing units should not exceed 8 meters.

R9: The system must work under all weather and illumination conditions. Reduced range in bad weather conditions.



4.5. Operational and environmental conditions R10: Nominal operating voltage is 12V for cars and 24V for trucks. 230V

are not allowed

R11: The functions have to be available without any errors between 9.8V to 16V for cars and 20V to 28V for trucks.

R12: The current consumption in the ignition OFF state should be lower than 100 µA.

R13: Operating temperature range is -40 to +85°C. H eating might be required.

R14: All components and connectors used must match the environment requirements (e. g. waterproof for outside mounting)

R15: Sealing against dust and spray water according to DIN 40050-9 is IP 5K 4K or IP 69K.

R16: Laser-based sensors shall be eye safety class I.

R17: Prototypes of the system must be so designed, that it will be possible to disable and completely disconnect them from function and CAN bus with immediate effect within a single operation of the driver.

4.6. Material requirements The following requirements are on the materials used in the sensor.

R18: The restriction for use of lead, mercury, hexavalent chrome and cadmium is to be held according to the EU 2000/53/EG directive.

R19: The scope of supply must not transfer health-hazardous or health-damaging substances in gaseous, liquid or solid state during storage, assembly and use.

R20: Lead-free soldering shall be used for all new developments and module variants. Not required for prototypes.

R21: Parts in the vehicle interior must be highly flame-resistant.

R22: Forces from fastening elements used in compliance with their intended application must not damage the material.

R23: All parts visible in the installed condition shall be free of faults, dust, dirt, scratches, etc.

R24: While in operation or travelling within functional temperature range the components must not generate any disturbing sounds not typical for operation mode.

4.7. Vibration requirements R25: For a device mounted on vehicle body, vibrations according to

Table 8 must not be the reason for any function to work incorrectly.



Table 8. Wide band random vibration profile values for body mounted parts.

Frequency [Hz]

Power density spectrum Severity 1

[(m/s2)2/Hz]


decreased by –3 dB

[(m/s2)2/Hz]


decreased by –6 dB

[(m/s2)2/Hz] 10 20 10 5 55 6.5 3.25 1.625 180 0.25 0.125 0.0625

300 0.25 0.125 0.0625 360 0.14 0.07 0.035 1000 0.14 0.07 0.035

R26: Mechanical shock pulse of 500 m/s2 for the duration of 6 ms must

not be the reason for any function to work incorrectly.

4.8. Requirements for wiring and contact pins R27: The push-on force for installation of the connector housing should

be < 100 N.

R28: If necessary, appropriate aids such as sliders or levers must be incorporated.

R29: The occurring forces must be compensated by the contact housing.

R30: To prevent galvanic corrosion, the same materials (uncoated contacts) or the same surface protection (coated contacts) shall be used for the plug connection (male/female connector).

R31: Golden contacts shall be used for:

• Signal currents below 3 mA. • Open-circuit voltages below 3 mV, application for open-circuit

voltages between 3 mV and 1 V is subject to agreement. • Signal currents below 25 mA and temperatures above + 130

°C • Signal currents below 25 mA and increased vibration stress.

R32: Design measures (e.g., spacing material, height of protective collar)

must prevent the contacts from being bent due to contact (even with mating coupling).

R33: The connectors shall be arranged on the housing in rows (single or multiple rows).

R34: To extend the current leak path, a plastic base, e.g. in a honeycomb pattern, shall be selected.



R35: The coding shall not be ensured by arranging the plug pins, but by the outside contour.

R36: The contacts shall be made solid (not folded), fixed and pressed in or injected.

R37: The plug connection must have a catch for the connector housing (on the cable side).

R38: Wiring list according to Table 9:

Table 9. Connector wiring. Pin Label Signal/Power Description tbd 15A V_Battery Power supply by ignition ON.

tbd 31 Ground Ground for power supply. tbd CANH CAN High Controller Area Network.

tbd CANL CAN Low Controller Area Network. tbd ON/OFF Switch HW switch for function toggling. tbd tbd tbd Reserve for additional I/O.

4.9. Electrical requirements R39: Microcontroller should keep at least 30% of its capacity (ROM,

EEPROM, RAM, computing power) as a back-up.

R40: The component shall contain software watchdog and processor external hardware watchdog to prevent the faulty function of software functions. Not required for prototypes.

R41: Data connection via CAN (terminated), CAN2.0 interface with transmission rate of 500 Kbit/s.

R42: To correctly distinguish between ignition ON and OFF, the signal has to be set at least 200ms without interruption.

R43: With every change of state ignition OFF to the state ignition ON the hardware reset has to be performed.

R44: Components with one microcontroller will start cyclic RAM, ROM and EEPROM check.

R45: Power-on or cyclic consistency check, detection of de-adjustment after shock is needed. Not required for prototypes.

R46: When reversed polarity is applied, no safety-relevant functions must be triggered.

R47: Reverse polarity must not cause damage.

R48: When detecting over-voltage or under-voltage, the device changes into secure state, i.e. no undefined function must appear during a phase of over-voltage or under-voltage. All functions of the device



automatically return to normal operation after returning into the operating voltage range.

R49: All electronic outputs must be protected against over-current.

R50: Device shall generally comply with relevant EMI standards for automotive equipment.



5. Conclusions This deliverable sets the requirements on the MiniFaros laser scanner from vehicle advanced driver assistance systems, ADAS, point of view.

Accident review and state-of-the art ADAS functions requirements show that there is big potential for the MiniFaros laser scanner to be used as sensor for these applications. As much as 54%-82% of accident scenarios for cars and trucks can be addressed by an ADAS function with sensor data provided by a MiniFaros laser scanner. This will, however, not mean that all of these accidents can be avoided but most of them will be avoided or at least reduced in its severity.

The state-of-the-art ADAS survey show that the general requirements on the laser scanner would approximately be:

• Range: 80 meters

• Range accuracy: 0.1 meters in near-field, 0.3 meters else

• Field of view: 250 degrees

• Angular accuracy: 0.25 degrees

• Update frequency: 25 Hz

Additionally more general requirements on the laser scanner are also specified because they will have to comply with automotive standards to be fitted and operated in vehicles.

In addition to the ADAS safety function there are a number of comfort functions with lower sensor requirements that also can be utilised by the MiniFaros laser scanner. That could for instance be stop and go functionality while driving in a queue in congestion of traffic and for parking assistance.

User needs shows that in order to get an increased penetration of ADAS functions especially in mid and lower class vehicle segments the systems have to be more affordable than sensor systems present on the market today.



References [1] ADASE: D2d Roadmap Development Version 1.0, 2004. IST-2000-

28010.

[2] PReVENT IP. IP-D15: Final Report, 2008. FP6-507075. http://www.prevent-ip.org

[3] COMPOSE. D51.11: Final Report, 2008. FP6-507075.

[4] APALACI. D50.10b: Final Report, 2007. FP6-507075.

[5] INTERSAFE. D40.75: Final Report, 2007. FP6-507075

[6] LATERAL SAFE. D32.11: Final Report, 2008. FP6-507075

[7] CARE – European Road accident Database

[8] Annual Reported statistics for Road Casualties in Great Britain, 2008

[9] National Highway Traffic Safety Administration, NHTSA, General Estimates System, GES, 2008

[10] Fatality Analysis Reporting System, FARS, 2008

[11] GIDAS, German In-Depth Accident Study. Accident database

[12] Report “Trafikolyckor med tunga lastbilar i Göteborg – fokus på oskyddade trafikanter” (Traffic accident involving heavy trucks in Göteborg – focus on unprotected road users)

[13] Volvo Accident Research Team: “Volvo 3P Accident Research Safety Report 2007”

[14] AUTOTECH CAST Europe, Harris Interactive 2006, European Consumer Advanced Automotive Technologies Report

[15] ISO 16750 –1, -2, -3, -4, -5, "Road vehicles - Environmental conditions and testing for electrical and electronic equipment"

[16] INTERSAFE-2 Deliverable D3.1: User Needs and Operational Requirements for a Cooperative Intersection Safety System, April 2009.

Date post:	29-Jun-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

User needs and operational requirements for MiniFaros ......assistance system Deliverable No. D3.1...

Documents