slides night vision presentation

8/10/2019 Slides Night Vision Presentation

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Copyright Autoliv Inc., All Rights Reserved

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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ALR JEK SAFER Nov 2008 # 3 Copyright Autoliv Inc., All Rights Reserved

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

[email protected]

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