Kay Ch. Fuerstenberg1; Pierre Baraud2; Gabriella Caporaletti3;Silvia Citelli4; Zafrir Eitan5; Ulrich Lages6; Christophe Lavergne7

1 University of Ulm, Department of Measurement, Control and Microtechnology, Albert-Einstein-Allee 41, 89081 Ulm, Germany, kay.fuerstenberg@e-technik.uni-ulm.de(before: IBEO Automobile Sensor GmbH, Fahrenkroen 125, 22179 Hamburg,Germany)

2 Peugeot Citroën Automobile, France3 EICAS Automazione S.p.A., Via Vela 27, 10128 Torino, Italy, gabry@eicas.it4 Fiat Research Center, Strada Torino 5O, 10043 Torino, Italy, s.citelli@crf.it5 TAMAM/IAI, Industrial Zone, 56100 Yehud, Israel, EZAFRIR@TAMAM.IAI.CO.IL6 IBEO Automobile Sensor GmbH, Fahrenkroen 125, 22179 Hamburg, Germany

UL@ibeo.de7 Renault SA, 1 Ave du Golf, 78288 Guyancourt, France,

Christophe.Lavergne@Renault.com

Abstract

In order to support, to guide and to validate the development of a pre-crash sensorial

system - necessary for near field crash detection in all scenarios - the CHAMELEON

project was established. The project, supported by the European Union, is running

from January 2000 until December 2002.

Several sensing technologies - like micro-wave radar, laser radar and artificial vision

- will be improved and evaluated for the detection of colliding objects on the sides

and the frontal area of the vehicle to calculate the crash probability. Information such

as vehicle type, distance, velocity vector and object orientation relative to the

equipped vehicle will be measured and considered for that prediction. A fusion

between the different sensors shall be evaluated for improved reliability of prediction,

and for operation under a wider range of environmental conditions.

Development of a Pre-Crash sensorial system –the CHAMELEON Project

Institut fuer KraftfahrwesenAachen

Peugeot Citroen Automobile(PSA)

Centro Studi sui Sistemi diTrasporto (CSST)

Israel Aircraft Industries –TAMAM

Conti Temic microelectronicGmbH

Regienov Renault RechercheInnovation

Volvo Car Corporation Porsche AG RAMOT (Tel Aviv) University

EICAS Automazione S.p.A IBEO Automobile Sensor GmbH Centro Ricerche Fiat S.C.p.A

SAAB Bofors (before: CelsiusTech Electronics AB) Thales A.S. (before: Thomson-CSF Detexis)

Table 1: CHAMELEON partners.

1 Introduction

More than 1,200,000 accidents occur every year in the European traffic with about

1,600,000 injured persons and 42,000 deaths [1],[2],[3],[4]. Studies estimated that

the annual rate of deaths and serious injuries was reduced by 120,000, since

introduction of passive safety systems.

PedestrianVs

Vehicle

IsolatedVehicles

VehiclesVs

VehiclesDead Injured Dead Injured Dead Injured

Austria 203 4,794 473 10,280 534 35,690Belgium 141 4,295 601 13,189 707 52,821

Denmark 115 1,111 133 2,098 334 6,782Finland 72 1,090 122 2,760 247 6,341France 971 21,657 3,007 35,541 4,434 124,205

Germany 1,281 42,982 3,946 124,927 4,227 344,232Ireland 115 1,830 125 1,931 197 8,912

Italy 851 16,870 1,888 39,445 3,773 203,256Netherlands 144 951 71 931 1,119 9,806

UK 979 46,386 819 43,889 1,967 228,191Spain 983 13,701 2,127 31,375 2,641 76,356

Sweden 67 1,244 174 4,196 331 15,733Switzerland 124 2,952 76 2,210 492 23,597

Total 6,046 159,863 13,562 312,772 21,003 1,135,922% 14.89 9.94 33.39 19.44 51.72 70.62

Table 2: European accident statistic [5].

But there are still too many people injured or dying in European traffic, as shown in

Table 2, caused by vehicles. To obtain a further improvement of accident prevention

and a reduction of both injuries and deaths innovative safety systems, as pre-crash

sensorial systems, must be integrated into future cars. The identification of a crash

approx. 100 ms before the impact would give quite a longer time to minimise the

injuries to the involved persons by deploying all the available protection means.

Statistics manifest that the frontal region of the vehicle is involved in 2/3 of the

accidents in Europe, as shown in Figure 1. This points to the need for a sensorial

system observing specially the frontal area of the vehicle.

21.0 %front

12.3 %offset

14.6 %angular

front19.0 %frontside

17.2 %angular

side

7.2 %side

3.6 %angular

back

2.3 %rear

2.8 %roll-over

Figure 1: Statistical analysis of distribution of car accidents in Europe [6].

The following chapter outlines the CHAMELEON project objectives. It is followed by a

presentation of the sensors used in this project. A discussion about the best sensor

configuration and the sensor performances is done in the next chapter, based on the

results of a simulation tool, testing the sensors in special defined - real world based -

accident scenarios, which are introduced as well.

2 Project Objectives

Main objective of the CHAMELEON project is to support, to guide and to validate the

development - also including the integration and adaptation concept - of a pre-crash

sensorial system essential for near impending crash detection in all scenarios, like

city, urban, rural and motorway. In this context:

• "to support" means to define a common concept for system requirements

including standard recommendations,

• "to guide" indicates the intention of the project to produce common EU guidelines

for system evaluation and validation, and

• “to validate” refers to the testing of the prototype system in real life situation, even

if in a controlled environment.

For the first two points the large participation of car manufacturers will strongly

contribute to reach EU common definitions.

Car design and equipment are affected fundamentally by EU legislation and the

quality of the car can have a major influence on the severity of injuries suffered by

people involved in accidents. The experience from the CHAMELEON project could

contribute to EU legislation, both in term of design and equipment for preventive and

passive safety [7].

There are two possible approaches to reduce the amount of accidents and/or to

depress the number of injured and dead people by vehicle engineering

improvements:

• the first is based on the concept of safety systems assisting the driver in avoiding

an accident (e.g. Blind spot observation, lane change assistant);

• the second consists in minimising the consequences of a crash once it is

unavoidable by the use of “reversible” or “irreversible” safety devices. The

„reversible“ approach (e.g. electrical seat belts pre-tensioning) does not imply

definitive damaging of the safety device. On the contrary, the “irreversible”

solution (e.g. air bags) require a substitution of the devices after use.

The CHAMELEON project can be associated to the second category of safety

applications. Thanks to the powerful sensing and processing of the detection

equipment and to the data fusion, the CHAMELEON system is supposed to identify

impending collisions in advance and to trigger the activation of on-board safety

devices, minimising crash consequences on people. The project is mainly oriented

towards “reversible” safety applications, which seem to be more promising than the

“irreversible” ones for short term market introduction.

The focus of the project will be to develop a prototype pre-crash system in terms of

functionality (scenarios to be covered, limits, uses and misuses) and architecture,

and to validate it.

It is also in the interest of the CHAMELEON project, near to the previously mentioned

concept “Safety system assisting the driver in avoiding an accident”, to investigate

the possible synergies of the pre-crash sensors with other Advanced Driver

Assistance Systems (ADAS) sensors, and consequently the benefits of these

synergies at functional level [8],[9]. This analysis is called within the Consortium

“Evolutionary Multi-Functional approach”. Current ADAS applications generally

involve only one dedicated sensor (e.g. longitudinal vehicle control - Adaptive Cruise

Control and Stop&Go - or lateral control - Blind spot and Lane warning/keeping). The

tendency for the future is to integrate in vehicle only a minimum number of sensors to

be used for different applications (“Multifunctionality” concept).

The challenge of the CHAMELEON Consortium is to consider at system level the

complete evolutionary application, investigating and defining from the beginning the

adequate next generation of sensorial system requirements.

Summarising, the CHAMELEON project is research oriented and has the following

objectives:

• Improvement of the already existing sensor performances (to cover the nearest

distance from the car and improve their detection time/update rate), of their

robustness (i.e. in terms of false alarms) and their operative range extension (e.g.

applying a single sensor approach or investigating the possibility of the data

fusion approach among different technologies: micro-wave, laser and

computervision);

• Study of new strategies for the activation of the safety restrain system, with the

aim of giving the possibility to pre-warn the safety actuators, reducing and

optimising their activation time when dangerous situations are detected and

before the occurrence of any crashes. The strategies for the activation of the

safety restrain system will also consider input from in-vehicle occupant sensors to

detect occupant out of position, presence, weight. This will lead to the

improvement of the existing protection systems by adding pre-crash information

for preventing or mitigating the effect of the crash;

• Study of the safety requirements that enable override of the system in case of

malfunction, including dependability;

• Validation of the system in a dedicated test site including the defined scenario

identified during the project life;

• Analysis of the integration of such system with other applications, in order to

optimise the sensor requirements for the different functions and identify the

optimal application synergies (Evolutionary Multi-Functional approach);

• Evaluation of achieved results with respect to social impact and productivity,

including costs/benefits;

• Investigation of legal and liability implications (in co-operation with other projects);

• Definition of Standard guidelines at protocol and architecture level, CEN/ISO

proposal drafting;

• Dissemination of the results through exploitation activities (Work-shops on the

dedicated test site, and participation to World Congress Show Case Exhibition

(e.g. ITS 2000) and Paper presentation, to raise public awareness).

The system must be driver independent. In fact it does not take in consideration the

driver behaviour, but it considers “only” the safety aspect. For this reason no user

acceptance will be analysed, because it is reasonable that every safety system of

this type is well accepted by the driver.

The project is well in line with the road map of the ADASE Umbrella group (Advanced

Driver Assistance Systems in Europe).

3 Sensors

Several sensing technologies will be evaluated for detecting colliding objects on the

sides and the frontal area to calculate the crash probability. The sensing technologies

will include micro-waves radar, laser radar and artificial vision.

Saab IBEO TAMAM Temic Thales A.S.

Technology micro-waveradar

laser rotatingradar

artificialvision

laser multi-beam radar

micro-waveradar

Scan rate 50 Hz 40 Hz 25 Hz 100 Hz 25 Hz

Delay time 20 ms 25 ms 40 ms 10 ms 40 ms

Aperture angle 100° 270° 60° 3 x 15° 60°

Field depth 0.5 - 20 m 0.3 - 20 m 0 - 40 m 0.5 - 6 m 0 - 60 m

Distance accuracy 0.1 m 0.05 m 3% 0.1 m 1 m, 5 %

Angle accuracy 10° 1° 1° 15° 2°

Velocity accuracy 5 % ±1 Kph 6% 10 Kph ± 0.2 Kph

Table 3: Sensor specification given by the sensor suppliers.

Information such as relative speed, outline, impact probability, time to impact, impact

location, geometry, impact angles, material, stiffness and mass (arranged by their

importance) will be measured and considered for the prediction of the obstacles

movement and its potential endangering for the vehicles passengers or the obstacle

itself.

As determined by the consortium, there are obstacles, which must be definitely

detected (cars, trucks, poles and trees). As well as obstacles that will be interesting

to be detected (walls, security rails, motorcycles and bicycles), and obstacles which

are not so important (animals, pedestrians and ditches), because they are not

triggering the pre-crash-system, but also being detected by the sensors in the

CHAMELEON project.

A fusion between the different sensing technologies shall be evaluated for improved

reliability of prediction, and for operation under a wider range of environmental

conditions.

3.1 Artificial Vision

Automatic Classification: A unique multispectral classification method will be used for

the classification of the neighbouring objects in order to help with the prediction of

risk. If an accident is predicted the classification of object, together with it's dynamic,

shall be used for the selection of the optimal mode of operation of the safety device.

This method is based on imaging the object with multispectral vision, which provides

much more information than standard vision techniques. This information is analysed

using a proprietary classification algorithm. The results are improved classification

performance and fully automatic classification. The multispectral method was

developed by one partner of the Consortium for thermal imaging (infrared). It will be

evaluated with artificial vision in the visible spectrum to produce a more cost-effective

system.

Stabilisation: A stabilisation of the sensors may be implemented based on

Consortium expertise in airborne optronic stabilisation. The requirement shall be

evaluated based on typical car vibrations. Cost effective methods shall be evaluated.

3.2 Micro-wave Radar

Millimetre-wave radar systems are well known since the early 1940th and had their

main applications almost only in the military region since many years. During the last

decade improved industrial manufacturing processes and the emerging industrial

applications like Satellite TV, Mobile Phones, GPS systems, etc. opened a high-

frequency market segment above the 1 GHz border. The steadily increasing transit

frequency of micro-wave circuits makes it now possible to use micro-wave radar

technology also for automotive applications in a high volume, low cost segment.

The main advantages of micro-wave-based automotive applications are the invisible

mounting capabilities behind non-conductive materials, the very high robustness

against harsh environmental conditions and the precise and fast acquisition of both

distance and speed information. The actual performance of radar devices for air-

force jets lets only imagine what such devices could yield regarding comfort and

security aspects to vehicles in some years. Within the project the possibility will be

investigated to use the micro-wave components developed for other ADAS functions

(based both on 77 GHz and 24 GHz) for the pre-crash application considering the

necessary resolution and accuracy improvements for the short range detection and

the new parameters (e.g. object location and identification) to be added. A new

sensorial system architecture will be designed, too.

3.3 Laserscanner

IBEO Laserscanners are used in several applications in the past with convincing

results [10],[11],[12],[13],[14],[15]. Because of the pre-crash application a high

dynamic Laserscanner for near field scanning will be developed. The new IBEO Pre-

Crash Laserscanner will measure distance, velocity, direction and outline of the

obstacle in a high resolution and

accuracy. Scanning with 40 Hz scan

frequency the angular resolution is

1.0°. To achieve a update rate of

25 ms with a viewing angle of up to

270° is one of IBEO’s aims in the

project. A truck driving 3 m ahead of

the test-vehicle is detected by more

than 40 measurement points, that

means a measurement point every

5 cm on the outline of the truck’s

back. The Laserscanner is eye-safe

(laser class 1) and has a single shot measurement accuracy of ± 5 cm (1 Sigma) with

a max. range of 20 m.

The Laserscanner creates a 2-dimensional range profile of the environment. The

built-in DSP allows a high speed object detection and the use of a high performance

object tracking algorithm for real-time tracking.

3.3.1 Object Detection and Tracking

Usually the measurement points of one revolution of the sensors head (scan) are

divided into clusters, which are assumed to belong to the same object, the so called

segments. These segments are represented by several parameters, e.g. left, right

and closest point to the sensor, which leads to a massive data reduction.

Comparing the segment parameters of the current scan with predicted parameters of

known objects from the scan(s) before quite a few of these objects will be

recognised. In our case a Kalman Filter is doing this job, calculating the longitudinal

and lateral velocity of the object as well. Unknown segments become objects,

starting with default dynamic parameters.

Test vehicle

12 m

10 m

8 m

6 m

4 m

2 m

14 m

0 m

-6 m-4 m-2 m0 m2 m4 m6 m

Test vehicle

3 / car0 / car

3 / car

3 / car

3 / car

11 / ped.

12 m

10 m

8 m

6 m

4 m

2 m

14 m

0 m

-6 m-4 m-2 m0 m2 m4 m6 m

Figure 2: Raw data outlining cars and a pedestrian on a three lane road (left).Object data of cars and a pedestrian of the same scene (right).

3.3.2 Object Classification

Object classification is done by distinguishing between typical object-outlines (static

data), like trucks, busses, cars, cyclists, motor-cycles and pedestrians. Having

additional knowledge about the static data of the past and also the dynamic

behaviour of the objects given by the tracking algorithm it is possible to achieve a

classification of the objects [16],[17].

To obtain a good classification it is essential to have reliable tracking algorithms.

Covering of objects can change the current outline of an object and interfere the

object tracking. Therefore, the classification and the detailed knowledge of the

objects parameters seen in the past is useful. Using this information a reconstruction

of the objects can create the real object-outline to assist object tracking and

classification [18].

An environmental model supports the selection of a suitable class [19],[20]. Also the

understanding of the traffic situation can help to find a classification [21].

3.4 Sensor Fusion

The output of all sensors such as micro-wave radar, laser radar and artificial imaging

(visible and thermal) is captured into the forecasting approach algorithm. This

integration may result in better detection and classification reliability than with each

sensor separately. Registration of the information from the different sensors is

required as a prerequisite to fusion.

Existing software tools from EICAS were adopted to create a dedicated EICASlab

CHAMELEON simulator to develop both sensor fusion software and test tools.

4 Simulation

Using the simulation the best sensor configuration for the project aims can be

identified, as well as the sensor performances can be tested and evaluated using the

sensor specifications from Table 3.

4.1 Sensor Configuration

All CHAMELEON partners agreed to define three initial sensor configurations,

according to each sensor capabilities. The goal is to define configurations which

allow the most covered surface and an information redundancy.

Celsius IBEOTamam

Thales A.S.Temic

Figure 3: Sensor configuration 1.

IBEOThales A.S.

Celsius

TamamTemic

Figure 4: Sensor configuration 2.

IBEO

Celsius

Celsius

Tamam

Thales A.S.Temic

Figure 5: Sensor configuration 3.

4.2 Testscenarios

The sensors in the CHAMELEON project will be evaluated, through simulated and on

test fields, in a set of real world based scenarios identified considering accident

statistics:

• Scenario 1 - frontal collision 75/75 kph (offset = 50%),

• Scenario 2 – frontal angled (30°) collision,

• Scenario 3 - frontal side (30°) collision 75/75 kph,

• Scenario 4 - lateral side (90°) collision 50/25 kph,

• Scenario 5 – Lateral collision (pole, tree),

• Scenario 6 – Lateral front collision (pole, tree),

• Scenario 7 - frontal collision 50 kph (wall).

Also laboratory crash tests will be applied in the simulation:

• US NCAP test – frontal collision 56 kph (rigid barrier, 100% overlap),

• US FMVSS208 test – frontal angled (30°) collision 56 kph (rigid wall, 100%

overlap),

• AMUS test – frontal angled (15°) collision 55 kph (rigid wall, 50%

overlap),

• EuroNCAP test – frontal collision 64 kph (deformable barrier (ECE

97/79), 40% overlap).

According to the Accident Analysis Report four different accident scenarios (1, 3, 4,

7) were chosen to be shown in this paper reproducing some real life situations, which

have been simulated.

The scenario 1, displayed in Figure 6, represent a main road with two bi-directional

traffic, two lanes each. The crash is due to a non respect of priority in a manoeuvre of

overtaking resulting in a frontal collision.

75 kph

75 kph

75 kph

75 kph

Figure 6: Scenario 1: Configuration before the crash (top). Frontal collision75/75 kph (overlapping 50 %)

75 kph

75 kph

Figure 7: Scenario 3: Configuration before the crash (top). Frontal side collision75/75 kph.

Scenario 3, illustrated in Figure 7, reproduces a bend of a road with a curvature of

about 40 m. The crash is due to a risky manoeuvre of overtaking while the visibility is

obstructed by a rock.

Scenario 4, shown in Figure 8, simulating a crossing with priority and stop signs as

well as other fixed objects (six trees and two walls): the vehicles 4 and 5 have to stop

at the crossing, but vehicle 5 does not, resulting in a crash with vehicle 1.

5

4

6 23 1

1

5

4

25 kph50 kph

Figure 8: Scenario 4: Configuration before the crash (top). Lateral collision50/25 kph.

Scenario 7, displayed in Figure 9, shows a crash due to a large lateral drift caused by

bad road conditions (rain, snow or ice). The vehicle 3 intends to turn right and

impacts directly into the wall.

6 23 1

30°50 kph

13

26

Figure 9: Scenario 7: Configuration before the crash (above). Frontal collision50 kph (wall).

The scenarios are shown in order to illustrate the power of the pre-crash simulation

tool. The sensors and their field of view are not drawn to keep simplicity, but of

course their performance is implemented as well.

4.3 System Definition

Following the iterative methodology described above, the simulation tool is used in

order to converge the definition of the pre-crash system, in terms of:

• number of sensors required,

• technical characteristics of sensors,

• sensor positioning on the vehicle,

• data fusion algorithms for sensor signal post processing,

As a first step of the iterative methodology the 3 initial sensor configurations are

tested. At this time, the focus is on the performances obtained with each sensor

taken separately, without any post processing (except for the crash prediction) or

data fusion strategy, in the scenarios defined previously. The first results are

summarised in Table 4.

Conf. Scen. 1 Scen. 2 Scen. 3 Scen. 4 Scen. 5 Scen. 6 Scen. 7 Limits

1 not used not used

not sogood(viewfield)

quite good good not used not used

2not so

good (onlyradial

speed)

good not used not used not used good

not sogood

(movingpoint)

CELSIUS

3not so

good (onlyradial

speed)

good

not sogood(viewfield)

quite good good good

not sogood

(movingpoint)

Onlyradialspeed,

one pointvariableby target

1 goodvery good

(objectoutline)

not used not used not used good good

2 goodvery good

(objectoutline)

very good(objectoutline)

good good good good

IBEO

3 not used not usedvery good

(objectoutline)

good good not used not used

Notavailable

if it israining.Goodobjectdicrimi-nation

1 not used not usedgood

(objectoutline)

quite good quite good not used not used

2good(angle

resolution)

good(objectoutline)

good(objectoutline)

quite good quite good quite good good (longrange)

TAMAM

3good(angle

resolution)

good(objectoutline)

not used not used not used quite good good (longrange)

Nodistance

andspeeddata.

Useful foridenti-fication

1 good good not used not used not used good good

2 good good not used not used not used good good

TEMIC 3 good good not used not used not used good good

Onlyradialspeed.

Detectionat closedistance

1very good

(longrange)

good not used not used not used good

not sogood

(movingpoint)

2 not used not used

not sogood (only

radialspeed)

quite good good not used not used

THOMSON

3very good

(longrange)

not sogood

(movingpoint)

not used not used not used good

not sogood

(movingpoint)

Onlyradialspeed,

onemovingpoint bytarget

Table 4: Sensor performances for different configurations and scenarios [22].

5 Outlook

The further work is focussed on the fusion of the data from the different sensors,

therefore a fusion strategy must be designed and implemented. The powerful

simulation tool will help to forecast the performance of the sensors and of the fusion

algorithm in real traffic scenes. A test vehicle will be built up, with a sensor

configuration presented before, to evaluate the sensor performance in real life

scenarios in order to find the best solution to obtain the crash probability and other

obstacle parameters introduced before.

References

[1] Promoting road safety in the EU - COM (97) 131 fin.

[2] Toward fair and efficient pricing in Transport – COM (95) 691 fin.

[3] Statistica degli incidenti stradali – 1997 Italy ACI ISTAT

[4] Statistisches Bundesamt – 1996 Germany

[5] Statistics of Road Traffic accidents in Europe and North America (the data ofGreece are not available)

[6] Tango, F.; Carrea, P.; Gobetto, E.: The Development of a Smart Pre-CrashSystem – the CHAMELEON project. Proceedings of ITS 2000, 7th WorldCongress on Intelligent Transport Systems, ITS 2000 Turin, Paper 2222.

[7] Technical Annex of CHAMELEON project – 1999 All Consortium members

[8] Langheim, J.; etc.: CARSENSE – Sensing of Car Environment at Low SpeedDriving. Proceedings of ITS 2000, 7th World Congress on Intelligent TransportSystems, ITS 2000 Turin, Paper 2054.

[9] Langheim, J.; Buchanan, A.; Lages, U.; Wahl, M.: CARSENSE - Newenvironment sensing for advanced driver assistance systems. Proceedings ofIV2001, IEEE Intelligent Vehicles Symposium, IV 2001 Tokyo, Paper 3-8.

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[11] Weisser, H.; Schulenberg, P.; Bergholz, R.; Lages, U.: Autonomous Driving onVehicle Test Tracks: Overview, Motivation and Concept. IEEE InternationalConference on Intelligent Vehicles, Stuttgart, 1998.

[12] Lages, U.; Fuerstenberg, K. Ch.; Willhoeft, V.: Nearfield Supervision all aroundthe vehicle: The Laserscanner and its abilities for Driver Assistance Systems.Proceedings of VDI-Conference The Driver in the 21st Century, Berlin, 2001

[13] Willhoeft, V.; Ewald, A.: Laserscanners for Obstacle Detection in AutomotiveApplications. Proceedings of IV 2000, IEEE Intelligent Vehicles Symposium,IV 2000 Dearborn, Paper IVS-008.

[14] Lages, U.: Collision Avoidance System for fixed Obstacles - Fuzzy ControllerNetwork for Robot Driving of an Autonomous Vehicle. Proceedings ofITSC 2001, IEEE 4th International Conference on Intelligent Transport Systems,ITSC 2001 Oakland, page 489-491.

[15] Willhoeft, V.; Fuerstenberg, K. Ch.: Quasi-3d Scanning with Laserscanners.Proceedings of ITS 2001, 8th World Congress on Intelligent Transport Systems,ITS 2001 Sidney, Paper 550.

[16] Fuerstenberg, K. Ch.; Hipp, J.; Liebram, A.: A Laserscanner for detailed trafficdata collection and traffic control. Proceedings of ITS 2000, 7th World Congresson Intelligent Transport Systems, ITS 2000 Turin, Paper 2335.

[17] Lages, U.: New Sensor for Stop & Go - Innovative Approach to PedestrianRecognition. Proceedings of ITS 2000, 7th World Congress on IntelligentTransport Systems, ITS 2000 Turin, Paper 2334.

[18] Fuerstenberg, K. Ch.; Willhoeft, V.: Pedestrian Recognition in urban traffic usingLaserscanners. Proceedings of ITS 2001, 8th World Congress on IntelligentTransport Systems, ITS 2001 Sidney, Paper 551.

[19] Dietmayer, K. C. J.; Sparbert J.; Streller, D.: Model based Object Classificationand Object Tracking in Traffic scenes from Range Images. Proceedings of IV2001, IEEE Intelligent Vehicles Symposium, IV 2001 Tokyo, Paper 2-1.

[20] Dietmayer, K. C. J.; Sparbert J.; Streller, D.: A System Architecture for theDetection and Modelling of Dynamic Traffic Scenes. Proceedings of ITS 2001,8th World Congress on Intelligent Transport Systems, ITS 2001 Sidney,Paper 78.

[21] Sparbert, J.; Dietmayer, K. C. J; Streller, D.: Lane Detection and Street TypeClassification using Laser range Images. Proceedings of ITSC 2001, IEEE 4thInternational Conference on Intelligent Transport Systems, ITSC 2001 Oakland,page 454-459.

[22] Piranda, B.; Caporaletti, G.: Deliverable D02.2 of CHAMELEON project: SystemConcept, 2001.