Session 22 Bo Wass

27
Copyright Autoliv Inc., All Rights Reserved En ny generation Night Vision Transportforum 2009 Bo Wass 8. January 2009

Transcript of Session 22 Bo Wass

Page 1: Session 22 Bo Wass

Copyright Autoliv Inc., All Rights Reserved

En ny generationNight VisionTransportforum 2009

Bo Wass8. January 2009

Page 2: Session 22 Bo Wass

ALV-Wass-Transportforum 2009 Copyright Autoliv Inc., All Rights Reserved

Innehåll

�Behovet av Night Vision (NV)

�Översikt IR-teknik för NV

�NV1

�NV2

�Exempel med NV 2

�Slutsats och nästa steg

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Behovet avNight Vision

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� UMTRI study (2001-33) based on FARS data 1987-1997

� 41597 fatalities (1999) sorted by type and % of occurrence at night.

0%

10%

20%

30%

40%

50%

60%

70%

Other vehicle

in motion

Pedestrian Overturn Tree

Total fatalities

Of which in darkness

Behovet av NV Hur definiera målet ?

Note that the effect of darkness is biased by other important factors such as traffic conditions and human behaviors

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Behovet av NVHur definiera målet ?

�Metod använd av UMTRI för att eliminera andra faktorer änmörker� “Daylight Saving Time” (DST) metoden

� Jämför perioder om 3 veckor före och efter vinter / sommartidsövergång

� Observations tid: 1 timma före och efter dag/natt eller natt/dagövergångar

� Andra parametrar antas i huvudsak konstanta

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Slutsats av UMTRI-studien�Riskökning vid bilkörning i mörker avseende olyckor med

dödlig utgång:� Kollision med fotgängare: 4.14

� Kollision med vilt: 4.60

� Kollision med annan bil i rörelse : 1.33

�Potentiell minskning av dödsolyckor i mörker per år genomatt eliminera effekten av mörker:� Kollision med fotgängare : 76%

*) based on 1999 US statistics

Behovet av NVHur definiera målet ?

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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 pedestrian fatalities

80% away from intersections

Location of fatal pedestrian accidents

67%% of pedestrian fatalities at night time

11%% of total traffic fatalities

Fotgängarolyckor med dödlig utgång

Source: IRTAD 2005, www.bast.de

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Day; 36%

Night; 47%

Dawn &

twilight;

17%

Source: CARE Database / ECDate of query: July 2005

Fotgängarolyckor med dödlig utgång i Europa

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RescueRide DownActivation of Protections

Automatic Braking

�All data indikerar att det första trafiksäkerhetsmålet förNight Vision ska vara att reducera fotgängarolyckor

�Systemets funktionalitet måste inkludera förbättrad“situation awareness” och varning.

Behovet av NV Slutsats

WarningAwareness

enhancement

impact

0

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Infrared (IR) Technology for Night Vision

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� FIR � “Far Infra Red” or “Passive IR”, (typically 8-12 mikrometer)

� All objects with own temperature above 0°K emit radiation according to Planck´s law (depending on object temperature and emissivity)

� FIR sensors only “listen” to the emitted radiation (Passive)

� Most materials are opaque to FIR (glass, plastic etc.), some specific materials are transparent (Ge, Si etc.)

�NIR � “Near Infra Red” or “Active IR” (typically 0,8 mikrometer)

� Works like visible light: sensors are capturing NIR reflected by objects

� Illumination e.g by modified halogen lamps

FIR och NIR för Night VisionGrundläggande funktion

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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

� Strongly affected by adverse weather conditions

FIR och NIR för Night VisionKaraktäristiska egenskaper

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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 och NIR för Night VisionEn jämförelse

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Autoliv FIR Night Vision 1Awareness enhancement

In production since 2005

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Autoliv Night VisionGeneration 2

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Viktiga förutsättningar

� successful experience with FIR NV camera and system

� real world need for early detection of Pedestrians at night

� intrinsic ability of FIR to detect living beings

Autoliv beslutade 2005 att starta produktutvecklingen av NV Generation 2 med fotgängarvarning

� internal development

� 2nd generation camera and ECU

�SOP October 2008

Fotgängardetektering och varningHistorik

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Krav: Varna “soon enough” med hög tillförlitlighet

� “Time to Impact” (TTI) : Interna studier visar att 2-4 s ärönskvärt (reaktionstid + agerande) beroende på hastighet� Ex, varning vid 3,5 s TTI motsvarar ca 88 m detekteringsavstånd vid

konstant 90 km/h

�Systemkrav detekteringsavstånd: 90 m

� “Warning-rate” : > 90 %

� Falska varningar: < 1 per 3 timmar (reference data set)

�Bra bildkvalitet

�HMI (designas av biltillverkaren)

Fotgängardetektering och varningKrav

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Använd kommersiellt tillgänglig sensor och processor

�Detector pixel count and FoV shall provide enough image-pixels to identify a pedestrian at 90m

�Processor & architecture� Fit for running the novel and innovative detection algorithms

� Must meet the high data stream demand

� Automotive grade

� Car specific functions should be well separated from the pedestrian detection software

Fotgängardetektering och varningTeknologibegränsningar

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Fotgängardetektering och varningNV2 System Lay-Out

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Fotgängardetektering och varningFIR Sensing & processing technology choice

Detector:

�Vanadium Oxide Bolometer (FLIR Systems)

�320X240 pixels

�25 µ pixel pitch for reduced cost

�High sensitivity : NETD<100mK

Optics:

�HFoV: 24 deg horizontal

Detection

�Algorithm containing Extractor, Classifier based on latest statistical learning theory, car’s Path Prediction and Tracker

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Fotgängardetektering och varningNV2 FIR Camera

Serviceable Serviceable

Front WindowFront Window

ShutterShutter

FIRFIR

DetectorDetector

Power &Power &

Video BoardsVideo Boards

OpticsOptics

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Fotgängardetektering och varningEstimering av TTI (och position) position

� X/Y Distance and Time-to-Impact estimator

� Examples: 1.8 m and 1.2 m “stationary” pedestrians, car speed 50km/h

� Actual system performance compared with reference sensor data

6 7 8 9 10 11 12 13 14 150

1

2

3

4

5

6

7

8

9TTI (s)

Sequence Time (s)6 7 8 9 10 11 12 13 14 15

-6

-4

-2

0

2

4

6Y distance (m)

Sequence Time (s)

True Estimated

1.8 m

8 9 10 11 12 13 140

1

2

3

4

5

6

7

8

9

10TTI (s)

Sequence Time (s)8 9 10 11 12 13 14

-6

-4

-2

0

2

4

6Y distance (m)

Sequence Time (s)

1.2 m

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Fotgängardetektering och varningVarningstrategi och HMI

The warning is activated when:

� a pedestrian is located in the “static” warning area

� a pedestrian moves into the vehicle path

� size of warning area is speed-dependent

An OEM-specific HMI is used to alert the driver

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Fotgängardetektering och varningVarningstrategi och HMI

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Fotgängardetektering och varningVarningstrategi och HMI

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Slutsats och nästa steg

Slutsats

� FIR is confirmed to be the best technology for detecting living beings

�Pedestrian Detection with single FIR camera is a world’s first

�Real World safety benefit expected to be high

�Successful development and SOP

Nästa steg

�Animal Detection & Warning

Page 27: Session 22 Bo Wass

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Tack!