Current Accident Analysis and AEB Evaluation Method for Pedestrians in Japan

Transcription

1 Final AsPeCSS Workshop Current Accident Analysis and AEB Evaluation Method for Pedestrians in Japan July 1st, 214 National Traffic Safety and Environment Laboratory Kenichi Ando

2 Outline Pedestrian accident in Japan Regulation and JNCAP (passive) Pedestrian accident analysis Evaluation method of AEB 2

3 Traffic Accident in Japan Fatalities Government Targets of fatality reduction Target of Target of Injured 781,494 Vehicle Fleet 74,434,936 Accidents 629,21 Fatalities 4, Accidents and Injured 3

4 Traffic Accident in Japan Comparison of Fatalities in 22 and 213 Vehicle occupants Motorcyclists Motorized bicycle Bicyclists Pedestrians Other 28.8% 2, % % % N = 8, % 3, % 1, N = 4, % 1, % 13.7% 6.7% National Police Agency 4

5 Injured Body Regions in Pedestrian Head,Face Neck Thorax,Back Arm Abdomen Pelvis Hip Leg other Whole body Injured 2.7% 8.% 2.2% 11.6% 33.5% N = 61,1 5.1% 1.% 56.3% 3.7% 16.8% 1.1% 7.4% Fatal N = 1, % 1.5% % 2% 4% 6% 8% 1% 213 ITARDA 5

6 Age in Pedestrian < < Injured 5.5% 24.1% 29.1% 13.6% 15.7% N = 64, % 7.8% 21.7% 2.3% 47.6% Fatal N = 1,634 < 1.5% % 2% 4% 6% 8% 1% 212 ITARDA 6

7 History of Regulation and JNCAP Regulation Head Leg EEC 23/12 Base UN-R127 Base JNCAP Head Leg EEC 23/12 Base UN-R127 Base 7

8 Trend of J-NCAP Pedestrian Protection Test Results Head Protection Performance Leg Protection Performance Level 1 Level 2 Level 3 Level 4 Level 5 Level 1 Level 2 Level 3 Level 4 Level 5 Number of tested vehicles 台数 Number of 台数 tested vehicles Introduction of regulation Introduction of regulation 8

9 Main Body Regions Injured among Fatal Pedestrians 1999 and 29 in Real-world Accidents < Year 1999 > < Year 29 > 1 n=94 n=165 n=68 n=149 n=246 1 n=459 n=139 n=42 n=279 n=17 Distribution (%) Sedan Mini van Box van Light passenger car Light cargo van Distribution (%) Sedan Mini van Box van Light passenger car Light cargo van Head Chest Hip Others 9

10 Technical Countermeasure for Pedestrian Protection Head Protection Test Fatal Accident in Japan To reduce the fatality, a reduction of impact velocity is a key. AEB for Pedestrian Detection is effective. Pedestrian 1,584 36% 4,373 Motor cycle 11% Moped 7% Car 1,415 32% Bicycle 6 14% Detection Year 213 (1) Near miss incidents (2) The effect of impact velocity reduction (3) The current performance of AEB Auto Alert & Braking 1

11 Analysis of Near-miss Incidents Data 11

12 Near-miss Incident Data from J-SAE Data: Driving recorders are installed in taxis 25 to 29: 38, incidents as Near-miss data 15 taxis in Tokyo 2 taxis in Shizuoka When a taxi driver brakes with high deceleration, the information are recorded for 15 seconds. Recorder installed Sudden brake camera 1 seconds 5 seconds Possibly recorded for 15 seconds Forward motion picture Car traveling velocity Brake signal Acceleration NTSEL: 163 car-to-pedestrian near-miss incident data were analyzed in the present study. 12

13 Near-miss Incident (walking) 13

14 Near-miss Incident (running) 14

15 Macro Data from ITARDA Macro Data: All recorded accident data reported to police in Japan Injury level Fatal, Serious, Minor Most severely Overall, Head, Face, Neck, Chest, injured part Abdomen, Waist, Arm, Leg Cause e.g., Steering, Glass, Ejection from a car Weather Sunny, Cloudy, Rain, Foggy, Snow Category Vehicle-pedestrian, Vehicle-vehicle, Vehicle by itself Road circumstance Straight road, Intersection Vehicle travel vel. Halt, up to 1km/h, up to 2km/h 15

16 Similarity of Car Traveling Velocity between Accident and Near-miss Incident Real world accident: 28 Minor Serious Fatal (n=61,1) Cumulative Percentage (%) (n=1,257) Minor (n=1,721) Serious Fatal Nearmiss Near-miss incident Near-miss (n=163) Car traveling velocity (km/h) Matsui et al. ESV 211 The car travel velocity of near-miss incident was similar to that of serious accident. 16

17 Moving Directions Between Vehicle and Pedestrian (1) On a straight road (2) At an intersection B B C C D D A A C C D D B B A B Cross in front of the moving car Go to the same direction as the moving car 17

18 Ratio (%) D C B A Similarity of Moving Directions between Accident and Near-miss 18% 29% 21% 16% 5% 35% 38% 39% 19% 32% 46% 31% 3% 67% 74% 78% 68% 38% C D Go to the same direction as the moving car A B Cross in front of the moving car Fatal Near-miss Fatal Near-miss accident incident accident incident Daytime Nighttime Hatching: intersection Matsui et al. ESV 211 About 7%pedestrians were crossing roads in front of the forward moving cars. Similarities are observed between accidents and near-miss incidents. We could estimate accident situations from near-miss incident data. 18

19 Classification of Near-miss Incidents 19

20 Definition of Time in Vehicle TTC and Pedestrian TTV Focused object Time Definition Reference V Vehicle Vehicle time to collision (Vehicle TTC) L Vehicle TTC = L V Matsui et al. ESV 211 Pedestrian Pedestrian time to vehicle (Pedestrian TTV) v Ld Pedestrian TTV = Ld v Matsui et al. Traffic Injury Prevention 213 2

21 Vehicle TTC from Near-miss Incident Data 11 near-miss incident data: Pedestrians were crossing roads. A drive recorder can capture forward motion pictures. Vehicle TTC = L V V L At a moment when a pedestrian appeared initiallyin front of a car in the video frame Assumption The worst situation was assumed that a car was moving toward a pedestrian without braking due to a car driver s carelessness. 21

22 Classified Four Pedestrians Stepping in Patterns to Find Out a Severe Condition using Vehicle TTC (1) (2) (3) (4) Unobstructed From behind From behind From behind view a building a parked vehicle a moving vehicle Car installing drive recorder 22

23 Results of Vehicle TTC in the Four Stepping in Patterns Avg Vehicle TTC (sec) Average Vehicle TTC km/h 2.sec 16.2m 24.3km/h 25.8km/h 32.9km/h 1.4sec Average car traveling velocity Average forward distance of a car & a pedestrian 1.3sec 8.6m 8.8m 1.2sec 11.1m (1) (2) (3) (4) (n=55) (n=8) (n=28) (n=1) Avgforward distance of a car & a pedestrian (m) Avg Velocity of a car (km/h) The vehicle TTC the longest in (1) from unobstructed view the shortest in (4) from behind a moving vehicle 23

24 Definition of Time in Vehicle TTC and Pedestrian TTV Focused object Time Definition Reference V Vehicle Vehicle time to collision (Vehicle TTC) L Vehicle TTC = L V Matsui et al. ESV 211 Pedestrian Pedestrian time to vehicle (Pedestrian TTV) v Ld Pedestrian TTV = Ld v Matsui et al. Traffic Injury Prevention

25 Calculation of Pedestrian TTV (Time to Vehicle) v Pedestrian TTV = Ld v Ld At a moment when a pedestrian appeared initiallyin front of a car in the video frame Assumption The worst situation was assumed that a pedestrian was moving toward a forward moving car line. 25

26 Results of Pedestrian TTV in the Four Stepping in Patterns Average pedestrian TTV Avg pedestrian TTV (sec) m 1.8sec 1.8m/s Average lateral distance of a car & a pedestrian Average walking speed 2.6m/s 2.m.8sec 2.2m 1.8m 2.1m/s 1.1sec 1.8m/s 1.1sec (1) (2) (3) (4) (n=55) (n=8) (n=28) (n=1) Avg lateral distance (m) Avg walking speed (m/s) The pedestrian TTV the longest in (1) from unobstructed view the shortest in (2) from behind a building 26

27 Comparison between Vehicle TTC and Pedestrian TTV in 4 Classified Stepping in Patterns Average vehicle TTC Average pedestrian TTV AvgTTC, TTV (sec) s 1.8s 1.4s 1.3s 1.1s 1.2s1.1s.8s (1) (2) (3) (4) (n=55) (n=8) (n=28) (n=1) *Matsui et al. ESV 213 Each of vehicle TTC and pedestrian TTV was similar in the 4 classified patterns. 27

28 Pedestrian Injuries and Collision Speeds 28

29 Flow for Relations of Fatality Risks and Impact Velocities Impact velocity micro data Travel velocity MACRO DATA Fatality risk Impact velocity Fatality risk 29

30 (km/h) Impact velocity Combined Results between Travel Velocity and Impact Velocity for Sedan - Micro Data Minor injury Serious injury Fatal (km/h) 1 y =.7496x y =.8929x y =.75 x y =.9484x R 2 = y =.89 x R 2 = y =.95 x R 2 =.8189 Impact velocity n = 5 2 n = 48 2 n = Travel velocity (km/h) Travel velocity (km/h) Travel velocity (km/h) Injury level Linear Regression Coefficient (km/h) 1 Minor Serious Serious Fatal Mean SD Number P-value.4*.18 Impact velocity Matsui, Oikawa and Ando STAPP Jnl 213 3

31 Vehicle Types Category Vehicle types Ordinary automobile (a) Sedan (b) Mini van (c) Box van Light automobile Engine displacement 66 cc (d) Light passenger car (e) Light cargo van (e) Light cargo van (d) Light passenger car 26% (c) Box van 4% 16% (a) 1,89 (b) Mini van 13% Sedan 41% Pedestrian fatal accidents in 29 in Japan 31

32 Fatality Risk as Functions of Travel Velocity/ Impact Velocities Fatality risk (%) Vehicle velocity (km/h) Impact velocity (estimated) (a) Sedan (b) Mini van (c) Box van Fatality risk (%) Travel velocity (macro data) Fatality risk (%) (d) Light passenger car Vehicle velocity (km/h) Vehicle velocity (km/h) Fatality risk (%) Fatality risk (%) Vehicle velocity (km/h) (e) Light cargo van Vehicle velocity (km/h) In case of fatal, driver did not brake enough so speed did not reduce drastically. 32

33 Fatality Risk as Functions of Impact Velocities Vehicle impact velocity (km/h) Fatality (%) Sedan Mini van Box van Light passenger car Light cargo van For the five types of vehicles, 3 km/h: The fatality risks areless than or equal to 5%. We would like to propose the specification of CDMBS: 3 km/h Matsui, Oikawa and Ando STAPP Jnl

34 Performance of AEB for Pedestrian Protection 34

35 Experiments for AEB Performance Frame material: FRP( Fiber Reinforced Plastics) Avoidance case Sensor Collision case 35

36 Test Conditions Items Test vehicle Detection sensor Test speed Environment Surface Dummy position Dummy orientation Dummy color A, B, C Conditions (A) (C) laser, camera and radar (B) stereo camera 5km/h 6 km/h (interval 5km/h) day, night dry, wet vehicle center, offsets front, side black, white, gray Standard condition 36

37 Scattered Results 7 Collision speed (km/h) Unstable Vehicle speed (km/h) 37

38 Binomial Test Results Collision Vehicle speed AEB Avoid Collision p θ ( c = 1 x) = exp( β + β1x) 1+ exp( β + β x) 1 θ = β, β ) ( 1 c = 1 : collision c = : avoid 38

39 AEB Performance by Binomial Logistic Regression 1 Probability of collision Vehicle A Vehicle B Vehicle speed (km/h) 39

40 Pedestrian/vehicle fatalities (26-21) fatality Pedestrian fatalities Hazard recognition speed(km/h) 4

41 Reduction of Fatality by AEB Pedestrian fatalities Reduction by Vehicle A AEB Fatalities Vehicle speed (km/h) 41

42 Three Groups of Test Results Severe Collision Vehicle speed AEB Minor Collision Threshold Avoid Collision 42

43 Total Risk of Severe Injury of Pedestrian Total Severe Injury Risk: P (head and chest) P(head or chest) = P (head)+p (chest)- P(head)*P (chest) Collision velocity 2 km/h 3 km/h 4 km/h 5 km/h (1) Medium Sedan 2% 5% 16% 35% (2) Minicar 2% 4% 5% 9% (3) SUV 2% 4% 8% 95% 43

44 Risk Distribution (Vehicle B) Probability Avoidance Minor collision Severe collision Vehicle speed (km/h) 44

45 AEB Effects on the Distribution of Pedestrian Fatalities (Vehicle B) 2,5 2, Avoidance Minor collision Severe collision Number of pedestrian 1,5 1, Vehicle speed (km/h) 45

46 46

47 Thank you 47

48 Fidelity Test: Radar Reflection Human Dummy 48

49 Deterministic vs. Probabilistic Caution! In some objects or environment, the pre-crash brake cannot recognize objects or does not work. Drive carefully, without relying on the pre-crash brake too much. 49