ntelligent vehicle technologies are moving to theforefront of technical innovation in modern-day vehicles. Some of these technologies arealready being deployed under theumbrella of the “driver assistance”

concept. This concept aims to aidhuman beings with certain dri-ving operations, such as auto-matic reverse parallelparking [1] or on-callautonomous driving basedon a vision camera [2].

A more advanced concept is theautonomous driving/intelligent vehi-cle concept. This enables a single vehicleto drive independently along the road [3].

Finally, the most advanced paradigm, thecooperative autonomous driving concept,enables a plethora of autonomous vehicles tocoexist on the roads and autonomously drive incooperation with each other. It is this latter concept thatis the focal point of one lab’s approach to intelligent vehicletechnologies. While several research groups are investigatingcooperative autonomous driving in the context of the automat-ed highway system, Griffith University’s Intelligent Control Sys-tems Laboratory (ICSL) distinguishes itself in two distinct ways:by developing solutions that operate in city environments and byemphasizing cooperative solutions to driving maneuvers thatwould enable autonomous vehicles to simultaneously coexist onthe roads with vehicles driven by human beings.

Another distinguishing element of the ICSL’s approach tointelligent vehicle technologies is that the algorithms current-ly enable vehicles to operate within the existing city infra-structure without the need for new road facilities. Thesealgorithms operate primarily on information provided byonboard vehicle sensors.

Cooperative Autonomous Driving Demonstration

In late December 2002, the ICSL successfullytested its cooperative autonomous driving

algorithms by deploying them onthree computer-assisted experi-

mental vehicle platforms devel-oped by the French scientific

organization INRIA andtheir industry partner

ROBOSOFT. The testswere undertaken in an out-

door environment in Rocquen-court, France, in cooperation withresearchers from INRIA’s IMARALaboratory. This event is believed

to be the world’s first on-roaddemonstration of cooperative driving

solutions for: 1) unsignalized intersectiontraversal and 2) an overtaking maneuver by

autonomous road vehicles designed for cities.The vehicles performed several driving maneu-

vers in cooperation with each other and without anyhuman assistance. The maneuvers included an unsignalized

intersection traversal, a cooperative overtaking maneuver, amaneuver requiring the vehicles to drive one behind eachother while maintaining distance and track control, and final-ly, a simple road-lane following behavior.

Figure 1 shows a picture of three experimental vehiclescrossing an unsignalized intersection. The vehicles each crossthe intersection in turn. They are able to successfully navigatethe intersection without collision by cooperatively determin-ing the order in which each vehicle traverses the intersection.

In Figure 2, the sequence of pictures shows the faster vehi-cle pulling out of the lane, moving alongside the slower vehi-cle, and finally overtaking the slower vehicle and resuming its

BY JONATHAN BABER, JULIAN KOLODKO, TONY NOËL, MICHAEL PARENT, AND LJUBO VLACIC

Cooperative Autonomous Driving

Intelligent Vehicles Sharing City Roads

1070-9932/05/$20.00©2005 IEEEIEEE Robotics & Automation Magazine MARCH 200544

Cooperative Autonomous Driving

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position in the original lane in front of the slower vehicle.This fulfills all the requirements of a cooperative overtakingmaneuver; the slower vehicle reduces its speed from themoment it realizes that it is being overtaken and then resumesits speed after the overtaking maneuver is complete.

The success of the experimental vehicle demonstration isattributed to the earlier design, development, and testing of theICSL cooperative autonomous driving maneuvers on the ICSLCooperative Mobile Robot platforms, as shown in Figure 3.

In the following sections, we describe the deployment ofthe ICSL algorithms on INRIA’s experimental vehicle plat-forms. We then explore the decision and control algorithmsfor cooperative autonomous driving maneuvers as developedby ICSL. Concluding remarks are then given.

The construct “intelligent vehicles” used within theremaining sections of this article relates to both the ICSLCooperative Mobile Robot platforms and the experimentalvehicle platforms because these platforms are manipulated bythe same decision and control algorithms. Therefore, theterm intelligent vehicle thus encompasses the concept ofcooperative autonomous driving.

Deployment of ICSL Hardware/Softwareon Experimental Vehicle PlatformThe demonstration was accomplished by interfacing ICSL’sdecision and control algorithms with INRIA’s experimentalvehicles. The two platforms communicate through the imple-mentation of a CAN bus module on each system to standard-ize communications. This configuration is shown in Figure 4.

Once the communication link is established, the decision andcontrol algorithms residing on ICSL’s hardware determine thecorrect speed and steering angle of the vehicle. These values aresent to the vehicle’s root processor and interpreted as commandsto update the vehicle’s speed and steering angle accordingly.

ICSL PlatformThe ICSL hardware consists of a number of modules, as

shown in Figure 5. Each microcontroller-based module is anode in the distributed architecture and represents a majorfunctional component. The modules include

◆ decision and control modules◆ motor-driving module◆ sensor-interface module◆ radio-communications module◆ power-supply module

Figure 1. An experimental vehicle unsignalized-intersection traversal sequence.

Figure 2. An experimental vehicle cooperative-overtakingsequence.

IEEE Robotics & Automation Magazine MARCH 200546

◆ sensor-carrier module◆ distance- and tracking-control module.

Experimental Vehicle PlatformFigure 6 gives an architectural overview of the experimentalvehicle platform developed by INRIA and its industry partnerROBOSOFT.

As shown in Figure 6, each wheel is surrounded by suffi-cient sensors to accurately determine the current speed andsteering angle. The front wheels are controlled by a Motorola

MPC555 microcontroller, and the rear wheels are controlledby a second Motorola MPC555 microcontroller. The twomicrocontrollers collaborate with a third x86-based embeddedPC, called the root processor. The three controllers are intercon-nected via the CAN Bus 1 network.

The software for the vehicle was designed using Synchro-nized Distributed Executive (SynDEx), a design tool forrapidly prototyping and optimizing the implementation ofreal-time embedded applications on multiprocessor architec-tures [4]. SynDEx operates under the Algorithm ArchitectureAdequation (AAA) methodology. Adequation is a French wordmeaning an efficient matching, as opposed to adequate, whichmeans a sufficient matching. The goal of the AAA methodol-ogy is to find the most efficient distribution of algorithmsover a multiprocessor architecture [5].

Cooperative Autonomous Driving ManeuversThe ICSL cooperative autonomous driving concept postulatesthat a number of autonomous vehicles may coexist on the road.In order to autonomously drive in cooperation with eachother, share the driving environments with vehicles driven byhuman beings, and behave in a way that is compatible withvehicles driven by humans, intelligent vehicles ought to be [6]:

◆ autonomous◆ computationally intelligent in order to

◆ interact with the surrounding environment◆ cope with incomplete information◆ adapt to unpredictable changes within the surround-

ing road traffic network environment.The following sections discuss the driving maneuvers that

enable the intelligent vehicles to behave autonomously andcooperatively.

As space limitations do not allow us to elaborate on theoriginal solutions designed for the ICSL cooperative mobilerobots, the remaining sections of this article address only themost relevant driving maneuvers included in the ICSL coop-erative autonomous driving concept. The details of the deci-sion and control algorithms as well as the sensory subsystemsmay be found in ICSL papers published elsewhere.

Road-Lane Following There are several motion control aims that must be fulfilled byintelligent vehicles performing road-lane following behavior

◆ Always more forward.◆ Do not leave the lane boundary.◆ Follow the lane direction as efficiently as possible.◆ Avoid an obstacle that may be in the lane. ◆ Avoid being delayed by slower moving intelligent vehicles.Figure 7 shows various corners and turns that intelligent

vehicles may encounter when following a road. The solution toroad-lane following behavior features the fusion of infrared andultrasonic sensor data into a rule-based motion algorithm [7].

Cooperative Overtaking The algorithm we developed fulfills the behavior require-ments of intelligent vehicles operating in a real-world

Figure 3. The ICSL cooperative mobile robots.

Figure 4. The block scheme of interfacing the ICSL hardwarewith an experimental vehicle.

ICSL CANBus

Interface

ICSL Hardware/Software

µController

µController

Computer

Experimental Vehicle

VehicleCAN BusInterface

CANBus

CANBus

MARCH 2005 IEEE Robotics & Automation Magazine 47

environment by enabling them toapproach, identify, and overtake aslower moving object [7]. Since thisis a cooperative algorithm, if theslower moving object is an intelli-gent vehicle, it will slow down fur-ther while being overtaken,facilitating the maneuver. Thisbehavior is equivalent to the provi-sion of an overtaking lane on ahighway, where slower movingvehicles like heavily laden trucksmay be overtaken. The overtakingmaneuver is shown in Figure 8.

Unsignalized IntersectionAn unsignalized road intersectionis where traffic flow is not gov-erned by traffic lights, stop signs,or give-way signs. To successfullynavigate an unsignalized intersec-tion without collision, two ormore intelligent vehicles mustcooperatively determine the orderin which they traverse the inter-section [8].

The developed solution uses pas-sive white lines to indicate the stop-ping positions and an event-drivendecision-making algorithm to deter-mine the order of intersection tra-versal. The algorithm precludes thepossibility of the vehicles becomingdeadlocked.

Distance and Tracking ControlThe distance and tracking control (DTC) concept is thecooperative behavior of two successive vehicles traveling inthe same direction in the same environment. This algorithmrequires one vehicle (designated the leader) to moveautonomously around its environment while another vehicle(designated the follower) maintains a coincident travel pathand desired longitudinal distance with respect to the leader.The followers must perform DTC without any interventionfrom external supervisory systems or humans. A fuzzy con-troller is used to implement this algorithm [7].

The developed solution for the DTC problem has so farbeen effectively demonstrated in both a stop-and-go-motiondriving maneuver (which is typical for city traffic-jam situa-tions) and platooning (where a number of vehicles form acolony in forward motion).

Obstacle AvoidanceIntelligent vehicles must be able to navigate around their envi-ronment, avoiding collisions with other vehicles and otherdynamic and static objects in that environment.

Typical approaches to the problem of obstacle avoidanceoften use either infrared or ultrasonic sensors but rarely useboth. The combination of the two sensors overcomes anyshortcomings of either sensor used individually and hasshown significant capability when implemented in mobilerobot-related applications. Each sensor complements theother to allow more robust obstacle-avoidance algorithms tobe implemented.

Dynamic Obstacle-Avoidance AlgorithmThe key limitation of the static obstacle-avoidance maneuveris that it cannot make intelligent navigation decisions indynamic environments. To overcome this limitation, twocomponents are necessary: 1) a sensor to determine thedynamics of the object and 2) a collision-avoidance schemeto utilize information from this sensor. We are currently test-ing a newly developed sensor that is able to segment thevisual environment into coherently moving regions in realtime while avoiding problems of temporal aliasing. This isachieved by processing a combination of visual and rangeinformation with an algorithm based on the optical flowconstraint equation [9] embedded in a robust statistical

Figure 5. The ICSL mobile robot architecture.

Sensor InterfaceModule

RadioCommunications

Module

Distance andTracking Control

Module

Motor DrivingModule

Two Motors

Two WheelEncoders

Two UltrasonicPosition Stepper

Motors

Sixteen InfraredReciever /

Transmitter Pairs

Optical SensorArray

Two Infrared WhiteLine Sensors

Two UltrasonicSensors

LED Display

SensorySubsystem

Laser Diode

Decisionand

Control ModulesI2C Bus

I2C Bus

I2C Bus

I2C Bus

framework. To ensure real-time performance with minimalsize and power consumption, a single field-programmablegate array (FPGA) is used to implement both the algorithmand the interfaces and glue-logic components. The result is acompact intelligent sensor operating at 10 Hz that is able todeal with a wide range of apparent velocities [10].

Concluding RemarksThis article presented the ICSL’s Cooperative AutonomousMobile Robot technologies and their application to intelligentvehicles for cities. The deployed decision and control algorithms

made the road-scaled vehicles capableof undertaking cooperative auto-nomous maneuvers.

The focus of the ICSL’s researchis in decision and control algo-rithms, not sensory input. It istherefore reasonable to considerreplacing or upgrading the sensorsused with more recent road senso-ry concepts as produced by otherresearch groups, such as IBEO’sALASCA onboard sensor [11].

While substantial progress hasbeen made, there are still someissues that need to be addressedsuch as: decision and controlalgorithms for navigating round-abouts, real-time integration ofall data (data fusion), and deci-sion-making algorithms to enableintelligent vehicles to choose thedriving maneuver as they go (toperform that driving maneuverwhich is, as they believe, themost appropriate for the giventraffic situation).

Regardless of the issues yet tobe addressed, the results of this

research on intelligent vehicle technologies have so far beenextremely encouraging. With continued research, it is feasi-ble that cooperative autonomous vehicles will coexistalongside human drivers in the not-too-distant future.

IEEE Robotics & Automation Magazine MARCH 200548

Figure 6. The experimental vehicle architecture.

x86“Root”

Microprocessor

MPC555“Front”

Microcontroller

MPC555“Rear”

Microcontroller

Rear RightWheel

Rear LeftWheel

Front RightWheel

Front LeftWheel

CAN Bus 1

CAN Bus 1

ICSL’s CAN BusModule

CAN Bus 2

Speed and AngleSensors

Speed and AngleSensors

Speed and AngleSensors

Speed and AngleSensors

Figure 7. A road-lane following maneuver.

Top View of Lane and Robot

Figure 8. An overtaking maneuver.

Direction ofTravel

(in Black)

SlowerMovingVehicle

OvertakingVehicle

OvertakingManeuver (in Red)

Top View

AcknowledgmentsA number of people participated in different stages of thedesign and development of the ICSL Cooperative MobileRobots and/or assisted in deploying the ICSL’s hardware/soft-ware solutions and testing the ICSL cooperative autonomousdriving concept on experimental vehicles.

Both Michel Parent and Tony Noël from INRIA andMark Hitchings and Wayne Seeto from the ICSL gave invalu-able assistance in testing and deploying the ICSL hardwareand software on the demonstration vehicles during the exper-imentation phase at INRIA, France. The design and develop-ment of the ICSL cooperative mobile robots were assisted atvarious stages by Mark Hitchings, Zivan O’Sullivan, JasonAdcock, Anthony Engwirda, and John Van Gulik.

The work was financially assisted in part from contribu-tions given by DEST—Innovation Access Program—International S&T, The Australian Academy of Science, TheAustralian Research Council (infrastructure), AusIndustry, andthe French Embassy in Australia.

KeywordsIntelligent vehicles, cooperative autonomous driving, cooperativemobile robots, decision and control algorithms.

References[1] I.E. Paromtchik and C. Laugier, “Motion generation and control for

parking an autonomous vehicle,” in Proc. IEEE Int. Conf. Robotics andAutomation, Tokyo, Japan, 1996, pp. 13–18.

[2] A. Broggi, “ARGO prototype vehicle,” in Intelligent Vehicle Technologies,L. Vlacic, M. Parent, and F. Harashima, Eds. London: Butterworth-Heinemann, 2001, pp. 446–493.

[3] L. Vlacic, M. Parent, and F. Harashima, Eds., Intelligent Vehicle Technolo-gies. London: Butterworth-Heinemann, 2001.

[4] Syndex Web site [Online]. Available: http://www-rocq.inria.fr/syndex/[5] Y. Sorel, “Algorithm architecture adequation methodology” [Online].

Available: http://www-rocq.inria.fr/~sorel/work/ fond99gb.html[6] L. Vlacic, A. Engwirda, M. Hitchings, and Z. O’Sullvan, “Intelligent

autonomous systems: Griffith University’s Creation,” in IntelligentAutonomous Systems, Y. Kakazu et al., Eds. Hokkaido, Japan: IOS Press,1998, pp. 53–60.

[7] M. Hitchings, L. Vlacic, and V. Kecman, “Fuzzy Control,” in IntelligentVehicle Technologies, L. Vlacic, M. Parent, and F. Harashima, Eds. Lon-don: Butterworth-Heinemann, 2001, pp. 291–331.

[8] L. Vlacic, A. Engwirda, and M. Kajitani, “Cooperative behaviour ofintelligent agents: Theory and practice,” in Soft Computing & IntelligentSystems, N.K. Sinha and M.M. Gupta, Eds. New York: Academic,2001, pp. 279–307.

[9] H.B. Schunk, “Determining optical flow,” MIT Artificial IntelligenceLab, Boston, MA, AI Memo 572, Apr. 1980.

[10] J. Kolodko and L. Vlacic, Motion Vision: Design of Compact Motion Sens-ing Solutions for Autonomous Systems Navigation. London: The Institu-tion of Electrical Engineers, 2005.

[11] IBEO Automobile Sensor GmbH [Online]. Available:http://www.ibeo-as.de/

Jonathan Baber completed both his B.Eng. degree in micro-electronic engineering and B.Inf Tech degree with first-classhonors at Griffith University, Australia. He is currently work-

ing towards his Ph.D. in control systems engineering. Hisresearch interests are in the study of control systems forautonomous robots.

Julian Kolodko completed both his B.Eng. degree inmicroelectronic engineering and B.Inf Tech degree withfirst-class honors at Griffith University, Australia. He is cur-rently completing his Ph.D. work: designing and imple-menting a sensor specifically designed for detecting andestimating motion for autonomous navigation purposes. Hisresearch interests range from digital signal processing to theprincipals of multirobot cooperation.

Tony Noël received his diploma in robotics engineeringfrom the University Pierre et Marie Curie, Paris VI, withclass honors. He participated in the construction of the Syn-DEx software and is currently working in the IMARA pro-ject as an engineer specializing in real-time control ofdistributed systems. He is in charge of the development ofthe CyCab platform.

Michael Parent is currently the program director atINRIA of the research and development team on automat-ed vehicles. He is also the program coordinator of theEuropean project CyberCars (http://www.cybercars.org).He led the development of a new type of vehicle called theCyCab which includes drive-by-wire and automatic dri-ving and is now manufactured by Robosoft, a start-upcompany. He is the author of several books on robotics andvision and numerous publications and patents. He has anengineering degree from the French Aeronautics School(ENSAE), a master’s degree in operation research, and aPh.D. in computer science from Case Western ReserveUniversity, Cleveland, Ohio.

Ljubo Vlacic received the Grad Diploma in engineering,and the M.Phil. and Ph.D. degrees in electrical engineering(control) from the University of Sarajevo in 1973, 1976, and1986, respectively. Currently, he is a full professor and found-ing director of the Intelligent Control Systems Laboratory,Griffith University, Brisbane, Australia. His research interestsand contributions span the areas of control systems, decisiontheory, intelligent control, artificial intelligence, and computerand systems engineering with application to intelligent vehi-cles and transport systems, mechatronics, intelligent robotics,industrial automation, and knowledge management. He is afellow of the Institution of Electrical Engineers (FIEE) and afellow of the Institution of Engineers Australia (FIEAust). Hehas been a Senior Member of the IEEE since 1999.

Address for Cor respondence: Ljubo Vlacic, Intelligent Control Systems Laboratory, School of MicroelectronicEngineering, Griffith University, QLD 4111 Australia. E-mail: L.Vlacic@griffith.edu.au.

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