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A new generation ofNight Vision System:
PedestrianDetection and Warning
Jan-Erik Kllhammer
SAFER seminar, November 6, 2008
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The need
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Problem Statement
All cars today have an acceptable night vision system high
beams, although they can be improved1
High beams are however of very limited use due to oncomingtraffic
Main aim of Night Vision: Substitute the high beams when
meeting traffic prevents their use.
System must remain efficient when facing oncoming vehicles:system efficiency in dark country roads is not relevant because
drivers would switch high beams on2.
1 K. Rumar, Adaptive illumination systems for motor vehicles: Towards a more intelligent headlighting system, UMTRI-97-7, 1997.
2 B. Fleury, A high performance night vision system, Proceedings of Progress in automotive lighting, Darmstadt, Germany, 2003.
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Realistic detection distances in trafficto dark vertical objects
K. Rumar,Night vision enhancement systems: What should they do and what do we need to know?, Report no. UMTRI-2002-12, 2002.
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UMTRI study (2001-33) based on FARS data 1987-1997
Sorted by type and % of occurrence at night.
0%
10%
20%
30%
40%
50%
60%
70%
Other vehicle
in motion
Pedestrian Overturn Tree
Total fatali ties
Of which in darkness
Increased risk in darkness
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Several factors contribute to the
increased risk in darknessNight time driving overall 2-3 times more dangerous then day time. 3
main factors:
Increased alcohol involvement
Increased fatigue/sleepiness/cardiac rhythm
Decreased visibility
J.M. Sullivan, M.J. Flannagan, Characteristics of pedestrian risk in darkness, Report no. UMTRI-2001-33, 2001.
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How to define the target ?
Method used by UMTRI to eliminate other parameters
Daylight Saving Time (DST) method
compare 3 weeks periods just before and after winter / summer time
changeover observation time: 1 hour before/after day/night or night/day transition
other parameters remain relatively constant
Results:
Pedestrian fatality risk ratio 4.14 in darkness vs. light
Animal fatality risk ratio 4.60 in darkness vs light
Fatality risk ratio involving other vehicles in motion: 1.33
Winter timeWinter time
Summer timeSummer time 5 pm 6pm 7 am 8 am
7 am 8 am
comparecompare
5 pm 6pm
J.M. Sullivan, M.J. Flannagan, Characteristics of pedestrian risk in darkness, Report no. UMTRI-2001-33, 2001.
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19000 700052002700Number of fatalities
2/5 > 65 y.o.
1/3 > 75 y.o.
71% away from
intersections
47%
14%
2/3 > 65 y.o.
1/2 > 75 y.o.
64% away from
crossings (*)
66%
32%
1/5 > 65 y.o.
1/10 > 75 y.o.
Elderly as pedestr ian
fatalities
80% away from
intersections
Location of fatal
pedestr ian accidents
67%% of pedestrian
fatalities at night t ime
11%% of total traffic
fatalities
Pedestrian FatalitiesReal World Data
Source: IRTAD 2005, www.bast.de
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Day; 36%
Night; 47%
Dawn &
twilight;
17%
Source: CARE Database / EC
Date of query: July 2005
Pedestrian Fatalities in EuropeInfluence of ambient l ight
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Infrared Technologyfor Night Vision
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Two types of Night Vision technologieson the market
Far Infrared (FIR): Detects radiation emitted by objects
Near Infrared (NIR):
Detects the reflected illumination from objects
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FIR
Strange image from thermal contrast in the scene
No illumination, hence no disturbance to other cars
No glare from head lights (FIR is blocked by glass and plastics)
Very long range from low attenuation in air
Intrinsically fit for detection of living beings
Moderately affected by adverse weather conditions
NIR
Conventional image from grey contrasts but living beings are hard to
identify from the scene
Illumination is a potential problem for oncoming NIR-NV equipped cars
Glare from head lights (partly removable by image processing)
Range limited by illuminator range and more limited by attenuation in air
More affected by adverse weather conditions
FIR and NIR for Night VisionHow they compare
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UMTRI-2004-38 Pedestrian Detection with Near
and Far Infrared Night Vision Enhancement
Distance
40m
80m
120 m
160 m
240 m
200 m
Easier detection using FIR
Younger drivers
Older drivers
FIR and NIR for Night VisionHow they compare
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Pedestrian detection and visual clutter
O. Tsimhoni, M.J. Flannagan, Visual clutter in active night vision systems reduces detection distance.
Proceedings of 6th international symposium on automotive lighting, Darmstadt, Germany, 2005.
A higher number indicates a higher rated clutter
FIR
NIR
Reduced clutter may improve the detection of pedestrians and animals
Preferred to support the drivers detection task.
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Autoliv Night Vision
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Autoliv FIR Night Vision 1Awareness enhancement
In production since 2005
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Gen 2 System Functionality
Enhance driver awareness
Warn the driver for pedestrians
in risk zone
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Example of events
Example of pedestrian detection
sequences
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Requirements: warn soon enough and reliably
Time to Warning: > 3.5 sec prior to potential impact
full stop time for initial speed of 80 km/h incl. driver reaction time
Detection Range: 90 m
Undesired warning: less than 1 per 3 hours of driving (mixed
reference data set)
High image quality
HMI (responsibility of car manufacturer)
Generation 2 with Pedestrian Detection & WarningRequirements and constraints
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Pedestrian Detection & WarningFIR Sensing & processing technology choice
Detector:
Vanadium Oxide Bolometer (FLIR Systems)
320X240 pixels25 pixel pitch for reduced cost
High sensitivity : NETD
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HMI &Human Factors Research
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HMI BMW 7 series
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Which Pedestrians to warn for?Are warnings to all pedestrians equally welcome?
What pedestrians to warn for not obviousAppropriate warning depending on frequency of warnings
Video clip: Annoying frequent alerts
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Pedestrian Detection & WarningWarning Strategy and HMI
The warning is activatedwhen:
a pedestrian is located inthe static warning area
a pedestrian moves into thevehicle path
size of warning area isspeed-dependent
Warning strategy and HMI interrelated
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Outlook
C S
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Deer-Vehicle Collisions in the USA
0
10 000
20 000
30 00040 000
50 000
60 000
70 000
80 000
AnimalF
atalities
WI PA MI OH IL
States
Animal Fatalities in USA 1991States with highest fatality rates
Accidents 1982
Accidents 1991
Romain L A, Bissonette JA Deer-Vehicle Collisions: Status of State Monitoring Activities and Mitigation Efforts,
Wildlife Society Bulletin 24: 276-283, 1996
500,000 Deer
Traffic Fatalities
in USA 1991
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Pedestrian Detection applicable also in near zone
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EU Pedestrian Protection Regulationproposal Oct 2007
Source: Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL
on the protection of pedestrians and other vulnerable road users, p. 10, 17
http://eur-lex.europa.eu/LexUriServ/site/en/com/2007/com2007_0560en01.pdf
Page 10:
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EU directive on Pedestrian Protection
Source: ACEA Proposal and justification to EU Enterprise Directorate-General
consultation on EU Directive 2003/102/EC Phase 2
Collision speed reduced
from 50 to 31 km/h
=> fatality risk reducedfrom ~33% to 5%Assuming braking force
0.6 g 10 m before impact
Pedestrian recognition
10 m0 m
Infrared SensingModule
fatalities rates reducerapidly with lower impact
velocity.
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Estimated effectiveness relative to current phase 2 of PedestrianProtection directive
Source: Feasibility Assessment commissioned to TRL by the EC, p. 235:
http://ec.europa.eu/enterprise/automotive/pagesbackground/pedestrianprotection/final_trl_2006.pdf
11.6% pedestrians fatalities would be saved with mandatory Brake Assist.
BAS only work in
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Activation ofActive Pedestrian Protection
500ms activation time enable simple reversible solutions
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Conclusions
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Conclusion
Main issue how well the system solve the drivers problem
with insufficient visibility under low beam conditions,
especially of pedestrians and other vulnerable road users.
System cost is the main obstacle for wide spread market
penetration of FIR systems.
Increased functionality and broader applications will result in
increase production volume which will drive down the unitcost.
New detector technologies promise significant cost down
possibilities.
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Thank youfor your attention
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