Investigation on Control Methods and Development of
Intelligent Vehicle Controller for Automated Highway
Systems
P.Suresh
ME11D045
Guide
Dr. P. V. Manivannan
Precision Engineering and Instrumentation Laboratory
Department of Mechanical Engineering
Indian Institute of Technology Madras
Chennai – 600036
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Contents
Introduction
Intelligent Vehicles
Objectives of Research
Success Stories
References
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Contents
Introduction
Challenges in Road Transportation:
Traffic Congestion
High accident rate
High Accident mortality
Increase in Vehicle Population
Government initiatives to tackle surface transportation problems:
Conversion of 2 lane highways to 4 lane highways
Building high speed expressways
Golden Quadrilateral project
Strict enforcement of traffic regulations
These Initiatives have not been able to solve the problems.
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Introduction
The Single Most Important Factor for Road Accidents is:
Human Error
Drivers tend to commit errors when driving at very high speeds due to the
following reasons:
Fatigue
Poor Visibility
Deteriorated Road Conditions
Adverse Weather
Very small available response times
Intelligent vehicles and Automated Highway Systems (AHS) will help in
reducing the number of accidents and thus minimise loss of life and property
resulting from accidents.
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Introduction
History of AHS & Intelligent Vehicles 1939 – New York World Fair – GM Futurama
first formal introduction to the idea of AHS and autonomous vehicles
1953 – scale model of AHS developed by GM and Radio Corporation of America
1958 – GM tests full size passenger car with in-built guidance system
1960s – Development of road centralised control system (Dr. Valdamir Zworykin from RCA).
Circuits buried in the road to magnetically sense vehicle speed and location.
1980s – a vision-guided Mercedes-Benz robot van, designed by Ernst
Dickmanns, Bundeswehr University Munich
DARPA-funded Autonomous Land Vehicle (ALV) in the United States
1987-1995- EUREKA Prometheus Project on autonomous vehicles
2000- US Army funded DARPA DEMO I,II and III projects for autonomous vehicles over difficult road terrain.
2000-AHSRA Demo 2000 (Japan) - 38 cars, buses and trucks illustrated the ideal system for reducing road traffic
accidents using driver information and control assist systems. The automation system made use of magnetic
sensors on the road.
2001--the Carnegie Mellon University Navlab project-semi-autonomous car
2001-2003-Chauffeur II –development of truck platooning by DaimlerChrysler, Renault, IVECO and Fiat
2009- SARTRE project-UK, Spain & Sweden- Platooning system with lead vehicle controlled by a professional driver
2010 -VisLab ran VIAC, the VisLab Intercontinental Autonomous challenge, 13000 KM test run of autonomous vehicles.
2011- First successful trial of SARTRE project was in Jan2011 at Volvo Test Track in Sweden-single car was slaved
behind a rigid truck.
1994- twin robot vehicles Vamp and Vita-2 of Daimler-Benz drove semi autonomously
1995-the Carnegie Mellon University Navlab project-semi-autonomous car
1996-Alberto Broggi of the University of Parma launched the ARGO Project – lane following
achieved
1997-DEMO 97- Fleet of over 20 vehicles guided on a 7 mile stretch of Interstate 15 Highway north of
San Diego, USA
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Introduction
Intelligent Vehicles (Driverless Cars)
Vehicles equipped with an autopilot system --- capable of driving without
input from a human driver.
Advantages of intelligent / autonomous vehicles are:
Relief from driving and navigating task
Accident prevention
Increased roadway capacity
Traffic congestion reduction
Increased safety due to elimination of driver error
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Intelligent vehicles
Intelligent Vehicle Sub-systems
Master controller
Steering controller Braking controller
Obstacle
avoidance
controller
Engine
controller
(ECU)
Transmission
controller Lane following
controller
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Intelligent vehicles
Fig. 1 Vehicle sub-systems
Sensors
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Intelligent vehicles
Perception is an important aspect of any intelligent or autonomous
system.
Some of the sensors used in the Intelligent Vehicles are:
GPS (Global Positioning System)-
• provides the absolute location and direction of the vehicle on the
road
• data is received from satellites orbiting the earth
Optical Camera - Eye of the vehicle. Provides it vision capability
Infra red camera - Provides night vision capability
Radar – Measurement of distances (vehicles -vehicle and Vehicle – obstacles)
Sensors
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Laser Scanner – Known as LIDAR (Light Detection And Ranging) Sensor.
• Most popular sensor, used in most vehicles.
• Very expensive (a typical unit costs more than 3 lakh
Rupees)
• Provides 2D and 3D data of the vehicle surrounding
environment
Odometry – measurement of changes in position, velocity and acceleration
using sensors located in moving parts.
Inertial Measurement Systems – Accelerometers and Gyroscopes
• Measures relative movement of robot in linear or angular
direction.
Compass – Determines vehicle direction with respect to earth’s poles.
Intelligent vehicles
Sensors
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Sensor fusion – Mechanism or algorithm that combines the data from
different sensors into one perception of the environment
Microwave
Radar
LIDAR
Infrared camera
Sensor
Fusion
Mechanism/
Algorithm
Driving
software
Perception
Optical camera
GPS
Gyroscope
Wheel encoder
Compass
Vehicle
mechanics
Intelligent vehicles
Fig. 2 Vehicle sensor fusion mapping
Intelligent Vehicle Sensor Location
(Figure from Robotland Blog article “Pickup an autonomous taxi cab in Berlin with iPad” dated October 18 th,2011) 11/15
Intelligent vehicles
Autonomous Passat
by
Volkswagen, Germany
Fig. 3 Autonomous Passat
Vehicle Architecture
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Sensor
Sensor
Sensor
Sensor Fusion
Mechanism/
Algorithm
|
|
|
Driver
Behavior
Model
Perception
A general architecture of an intelligent vehicle.
Path Planner
Navigator
Driver / Pilot
Engine
controller
Route map &
destination information
Brake
Controller Transmission
Controller
Steering
controller
Driving
software
Physical
Vehicle
layer
Intelligent vehicles
Fig. 4 Vehicle Architecture
Replace the Human Driver with an Equivalent Intelligent
System:
Develop a Supervisory / Master controller that will co-ordinate the
functioning of the various sub-system controllers of the intelligent
vehicle.
• Development of Vehicle and Driver behavior Models (Finite State
Machine – FSM)
• Development of control algorithms : Fuzzy, Neural and Genetic
Algorithms
• Simulation and validation of developed control algorithms
• Development of Embedded controller
• Testing with scaled down vehicle model
Objectives of Research
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Objectives
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Actual Implementation – Success Stories
Success Stories
Google’s Autonomous Car project
• Fleet of robotic Toyota Priuses have covered more than 1,90,000
miles (3 lakh kilometers) in all types of road conditions with minimal
human input.
University of Berlin’s “MadeinGermany” VW Passat
• Autonomous taxis in Berlin
VisLab (Italy) – VisLab Intercontinental Autonomous Challenge
(VIAC)
• Four driverless vehicles -15,000 KM trip - Parma in Italy to Shanghai
China - July 26, 2010 - October 28, 2010 with virtually no driver
intervention.
1. Massimo Bertozzi, Alberto Broggi, Alessandra Fascioli, ‘Vision-based intelligent vehicles: State
of the art and perspectives’, Robotics and Autonomous Systems 32 (2000) 1 – 16,
01/02/1999.
2. Nikhi M Chakravarthy and Lawrence B Holder Jr, ‘Intelligent Cars’, Intelligent Environments
Presentation, Computer Science and Engineering Department, UTA, Spring 2003
3. Jameson M Wetmore, ‘Driving the Dream: The History and Motivations Behind 60 Years of
Automated Highway Systems in America’ , Automotive History Review, Summer 2003, pp. 4-
19.
4. Sadayuki Tsugawa, ‘A History of Automated Highway Systems in Japan and Future Issues ‘,
Proceedings of the 2008 IEEE International Conference on Vehicular Electronics and Safety,
Columbus, OH, USA. September 22-24, 2008
5. Ola Ringdahl, ‘Techniques and Algorithms for Autonomous Vehicles in Forest Environment’,
Dept. of Computing Sciences, Umea University, Sweden, ISSN-0348-0542, ISBN-978-91-
7264-373-4.
6. John Leonard, Jonathan How, Seth Teller, David Barrett, Chris Sanders,‘DARPA Urban
Challenge: Team MIT Development Plan’, October 27, 2006
7. Robotland blog,’Pick up an autonomous taxi in Berlin with iPad’,October 18,2011
8. Erico Guizzo,’How Google’s Self-Driving Car Works’,IEEE Spectrum, October 18,2011
http://spectrum.ieee.org/automaton/green-tech/advanced-cars/how-google-self-driving-car-
works
References
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References
THANK YOU