Development of a Pre-Crash sensorial system – the ... studies... · concept “Safety system...
Transcript of Development of a Pre-Crash sensorial system – the ... studies... · concept “Safety system...
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, [email protected](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, [email protected] Fiat Research Center, Strada Torino 5O, 10043 Torino, Italy, [email protected] TAMAM/IAI, Industrial Zone, 56100 Yehud, Israel, [email protected] IBEO Automobile Sensor GmbH, Fahrenkroen 125, 22179 Hamburg, Germany
[email protected] Renault SA, 1 Ave du Golf, 78288 Guyancourt, France,
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.
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