Understanding Collision Processes using Video Data Workshop on Comparison of Surrogate Measures of Safety Extracted from Video Data Nicolas Saunier [email protected]

January 12th 2014

Outline

1

Motivation

2

Methodology

3

Experimental Results using Video Data

4

Conclusion

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Motivation

Outline

1

Motivation

2

Methodology

3

Experimental Results using Video Data

4

Conclusion

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Motivation

Traffic Conflicts

A traffic conflict is “an observational situation in which two or more road users approach each other in space and time to such an extent that a collision is imminent if their movements ´ 1977] remain unchanged” [Amundsen and Hyden,

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Motivation

Traffic Conflicts

A traffic conflict is “an observational situation in which two or more road users approach each other in space and time to such an extent that a collision is imminent if their movements ´ 1977] remain unchanged” [Amundsen and Hyden, Several traffic conflict techniques and lack of comparison Issues caused by the (mostly) manual data collection process cost reliability and subjectivity: intra- and inter-observer variability

Mixed validation results

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Motivation

The Safety/Severity Hierarchy

Accidents F I PD

Serious Conflicts Slight Conflicts Potential Conflicts Undisturbed passages

Various surrogate safety measures ´ N. Saunier, Polytechnique Montreal

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Motivation

Past research [Davis et al., 2008]

There is some evidence that evasive actions undertaken by road users involved in conflicts may be of a different nature than the ones attempted in collisions

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Motivation

Past research: The Whole Hierarchy

´ 2006] [Svensson, 1998, Svensson and Hyden, ´ N. Saunier, Polytechnique Montreal

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Motivation

Past research: The Whole Hierarchy

Feedback and learning process: collisions with injuries occurred at the ´ 2006] signalized intersection [Svensson, 1998, Svensson and Hyden, ´ N. Saunier, Polytechnique Montreal

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Motivation

Objectives

Understand collision processes by studying the similarities of interactions with and without a collision to design better counter-measures develop better surrogate measures based on better-known relationships between interactions with and without a collision

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Motivation

Objectives

Understand collision processes by studying the similarities of interactions with and without a collision to design better counter-measures develop better surrogate measures based on better-known relationships between interactions with and without a collision

Methods collect large amounts of interaction data, in particular using video sensors design suitable interaction descriptors and safety indicators (obtained through a robust probabilistic framework) design suitable interaction similarity measures use and adapt data mining techniques to cluster the interactions

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Methodology

Outline

1

Motivation

2

Methodology

3

Experimental Results using Video Data

4

Conclusion

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Methodology

Interaction Descriptors

v2

U2

distance

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Δv

v1

1

U

θ

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Methodology

Rethinking the Collision Course

A traffic conflict is “an observational situation in which two or more road users approach each other in space and time to such an extent that a collision is imminent if their movements remain unchanged” For two interacting road users, many chains of events may lead to a collision It is possible to estimate the probability of collision if one can predict the road users’ future positions the motion prediction method must be specified

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Methodology

Motion Prediction

Predict trajectories according to various hypotheses iterate the positions based on the driver input (acceleration and steering) learn the road users’ motion patterns (including frequencies), represented by actual trajectories called prototypes, then match observed trajectories to prototypes and resample

Advantage: generic method to detect a collision course and measure severity indicators, as opposed to several cases and formulas (e.g. in [Gettman and Head, 2003]) [Saunier et al., 2007, Saunier and Sayed, 2008, Mohamed and Saunier, 2013, St-Aubin et al., 2014]

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Methodology

A Simple Example

t1

0.7 0.3

t2 0.4

2

0.6

1 ´ N. Saunier, Polytechnique Montreal

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Methodology

Collision Points and Crossing Zones Using of a finite set of predicted trajectories, enumerate the collision points CPn and the crossing zones CZm . Severity indicators can then be computed: X P(Collision(Ui , Uj )) = P(Collision(CPn )) Pn

P(Collision(CPn )) tn P(Collision(Ui , Uj )) P P(Reaching(CZm )) |ti,m − tj,m | pPET (Ui , Uj , t0 ) = m P m P(Reaching(CZm )) TTC(Ui , Uj , t0 ) =

n

[Saunier et al., 2010, Mohamed and Saunier, 2013, Saunier and Mohamed, 2014]

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Methodology

Similarity Measures

Traditional measures rely on fixed length descriptor vectors: extract agregated values from continuous time series indicator data considerable loss of information

Some measures naturally accomodate variable length vectors: Longest Common Sub-sequence

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Methodology

Need for Improved LCSS 20

20

15

15

10

10

5

5

00

5

10

15

20

00

5

10

15

20

The series in each plot have maximum similarity if using δ = +∞: this is desired in the plot on the left since it is an exact sub-sequence, but not on the right if the rate of change is taken into account ´ N. Saunier, Polytechnique Montreal

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Methodology

The Aligned LCSS 1.6 1.4

Time to Collision (s)

1.2 1.0 0.8 0.6 0.4 0.2 0.02.0

2.5

3.0

Time (s)

3.5

4.0

4.5

Example of alignment of two very similar real TTC indicators: LCSS2,d0.2 s = 0.2 and ALCSS2,d0.2 s = 1 ´ N. Saunier, Polytechnique Montreal

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Methodology

The Aligned LCSS 16

4.5

14

4.0 3.5 Time to Collision (s)

Distance (m)

12 10 8 6

2.5 2.0 1.5

4 22

3.0

1.0 3

4

Time (s)

5

6

LCSS+∞ LCSS2 ALCSS2

7

0.52.0

Distance 0.87 0.35 0.42

2.5

3.0

3.5

4.5 4.0 Time (s)

5.0

5.5

6.0

6.5

TTC 0.64 0.12 0.42

These real profiles are more similar using LCSS with infinite δ than using ALCSS and a finite δ ´ N. Saunier, Polytechnique Montreal

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Methodology

Clustering Algorithm

All algorithms operating on a similarity matrix may be used Custom algorithm with cluster prototypes [Saunier et al., 2007] 1 2

3

Indicators are sorted by length For each indicator, if its maximum similarity is lower than a threshold, create a new cluster with indicator as prototypes Otherwise, assign it to the most similar prototype

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Experimental Results using Video Data

Outline

1

Motivation

2

Methodology

3

Experimental Results using Video Data

4

Conclusion

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Experimental Results using Video Data

The Kentucky Dataset

Video recordings kept for a few seconds before and after the sound-based automatic detection of an interaction of interest 213 traffic conflicts and 82 collisions The existence of an interaction or its severity is not always obvious The interactions recorded in this dataset involve only motorized vehicles Limited quality of the video data: resolution, compression, weather and lighting conditions

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Experimental Results using Video Data

Road User Tracking

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Experimental Results using Video Data

Motion Prediction

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Experimental Results using Video Data

Motion Prediction

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Experimental Results using Video Data

Motion Prediction

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Experimental Results using Video Data

Collision Probability

Severity Indicators 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

2

3

4 Time (second)

5

6

2

3

4 Time (second)

5

6

TTC (second)

3.0 2.5 2.0 1.5 1.0 0.5

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Experimental Results using Video Data

Description of Interactions [?]

Categorical attributes Type of day Lighting condition Weather condition Interaction category

Interaction outcome

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Values weekday, week end daytime, twilight, nighttime normal, rain, snow same direction (turning left and right, rear-end, lane change), opposite direction (turning left and right, head-on), side (turning left and right, straight) conflict, collision

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Experimental Results using Video Data

Description of Interactions Numerical attributes Road user type passenger car van, 4x4, SUV bus truck (all sizes) motorcycle Type of evasive action No evasive action Braking Swerving Acceleration 3 attributes from ∆v 6 values from s ´ N. Saunier, Polytechnique Montreal

Units number of road users number of road users number of road users number of road users number of road users number of evasive actions number of evasive actions number of evasive actions number of evasive actions km/h km/h

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Experimental Results using Video Data

Distribution of Speed Attributes

50

Norm of the velocity difference Collision Conflict

60 50

40

Speed Collision Conflict

Speed (km/h)

DeltaV (km/h)

40 30

20

20

10

0

30

10

DeltaVmin

DeltaV

DeltaVmax

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0

Smin1

Smin2

S1

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S2

Smax1

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Experimental Results using Video Data

3 Clusters Interaction outcome

140

Collision Conflict

120

Number of interactions

100 80 60 40 20 0 ´ N. Saunier, Polytechnique Montreal

Cluster 1

Cluster 2

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Experimental Results using Video Data

Clusters: Speed Attributes

70

70 60

50

50

40

40

Speed (km/h)

DeltaV (km/h)

60

Norm of the velocity difference Cluster 3 Cluster 2 Cluster 1

30

30

20

20

10

10

0

DeltaVmin

DeltaV

DeltaVmax

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Speed Cluster 3 Cluster 2 Cluster 1

0

Smin1

Smin2

Understanding Collision Processes

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S2

Smax1

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Experimental Results using Video Data

Clusters: Interaction Category 140 120

Number of interactions

100

Interaction category Side straight Same dir. chang. Same dir. rear Same dir. turn R Same dir. turn L

Cluster 1: collisions, highest speeds, categories side straight and same direction turning right

80

Cluster 2: almost pure conflicts, lowest speeds

60

Cluster 3: collisions, medium speeds, categories same direction turning left and right and same direction changing lanes

40 20 0

Cluster 1

Cluster 2

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Cluster 3 Understanding Collision Processes

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Experimental Results using Video Data

Indicator Clustering using Aligned LCSS [Saunier and Mohamed, 2014]

Indicator

Threshold 

Distance (Dist) Speed differential (SD) Velocity angle (VA) Time to collision (TTC) Probability of Collision (PoC)

1m 1.5 m/s 0.15 rad 0.2 s 0.1

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Understanding Collision Processes

Minimum Clustering Similarity 0.23 0.4 0.4 0.3 0.5

Number of Clusters 6 4 4 4 6

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Experimental Results using Video Data

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Dist (m)

Dist (m)

50 Cluster 1 - 23.1%(28/121) 50 Cluster 2 - 42.7%(35/82) 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Cluster 3 0 . 0%(0 / 8) Cluster 4 - 42.1%(8/19) 50 50 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Cluster 5 38 . 5%(5 / 13) Cluster 6 - 11.5%(6/52) 50 50 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Dist (m)

Dist (m)

Dist (m)

Dist (m)

Indicator Clustering using Aligned LCSS

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Experimental Results using Video Data

Indicator Clustering using Aligned LCSS Cluster 1 - 27.6%(42/152)

20

20

15

15

10 5 02

10 5

4

6

8 10 12 Time (s) Cluster 3 - 37.5%(21/56)

14

0 25

20

20

15

15

SD (m/s)

SD (m/s)

25

10 5 02

Cluster 2 - 30.0%(18/60)

25

SD (m/s)

SD (m/s)

25

0

2

4 Time (s)

6

8

Cluster 4 - 3.8%(1/26)

10 5

0

2

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4 6 Time (s)

8

10

00

2

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4 6 Time (s)

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10

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Experimental Results using Video Data

VA (deg.)

180 Cluster 2 - 23.0%(20/87) 160 140 120 100 80 60 40 20 0 2 0 2 4 6 8 10 12 14 Time (s)

180 Cluster 3 - 17.5%(10/57) 160 140 120 100 80 60 40 20 0 2 0 2 4 6 8 10 12 14 Time (s)

180 Cluster 4 - 33.3%(16/48) 160 140 120 100 80 60 40 20 0 2 0 2 4 6 8 10 12 14 Time (s)

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VA (deg.)

VA (deg.)

180 Cluster 1 - 35.6%(36/101) 160 140 120 100 80 60 40 20 0 2 0 2 4 6 8 10 12 14 Time (s)

VA (deg.)

Indicator Clustering using Aligned LCSS

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Experimental Results using Video Data

4.5 Cluster 2 - 38.2%(55/144) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

Cluster 3 - 33.3%(3/9) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

Cluster 4 - 5.0%(1/20) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

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TTC (s)

TTC (s)

TTC (s)

4.5 Cluster 1 - 19.4%(13/67) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

TTC (s)

Indicator Clustering using Aligned LCSS

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Experimental Results using Video Data

1.0 0.8 0.6 0.4 0.2 0.0 2 1.0 0.8 0.6 0.4 0.2 0.0 2

1.0 0.8 0.6 0.4 0.2 10 0.0 2

Cluster 2 - 45.3%(34/75)

PoC

Cluster 1 - 18.6%(24/129)

4 6 8 Time (s) Cluster 3 - 18.2%(2/11) 0

2

2

1.0 0.8 0.6 0.4 0.2 10 0.0 2

2

1.0 0.8 0.6 0.4 0.2 10 0.0 2

4 6 8 Time (s) Cluster 4 - 0.0%(0/5)

10

4 6 8 Time (s) Cluster 6 - 20.0%(1/5)

10

0

2

PoC

1.0 0.8 0.6 0.4 0.2 0.0 2

4 6 8 Time (s) Cluster 5 - 18.2%(2/11) 0

0

2

0

2

PoC

PoC

PoC

PoC

Indicator Clustering using Aligned LCSS

0

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4 6 Time (s)

8

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4 6 Time (s)

8

10

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Conclusion

Outline

1

Motivation

2

Methodology

3

Experimental Results using Video Data

4

Conclusion

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Conclusion

Conclusion

Better tools to measure interaction similarity (and general time series similarity)

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Conclusion

Conclusion

Better tools to measure interaction similarity (and general time series similarity) Mounting evidence that not all interactions should be used for surrogate safety measure

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Conclusion

Conclusion

Better tools to measure interaction similarity (and general time series similarity) Mounting evidence that not all interactions should be used for surrogate safety measure Future work: collect more data and compare methods

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Conclusion

Need for Open Science Scientific principle of reproducibility to what extent are the mixed validation results reported in the literature related to a lack of comparisons and reproduciblity of the various methods proposed for surrogate safety analysis?

Need to share data and tools used to produce the results public datasets and benchmarks public / open source software

Traffic Intelligence open source project https: //bitbucket.org/Nicolas/trafficintelligence

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Conclusion

Collaboration with Nadia Mourji, Bruno Agard, Mohamed Gomaa ´ Mohamed, Paul St-Aubin (Polytechnique Montreal) Funded in part by the Natural Sciences and Research Council of Canada (NSERC)

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Conclusion

Collaboration with Nadia Mourji, Bruno Agard, Mohamed Gomaa ´ Mohamed, Paul St-Aubin (Polytechnique Montreal) Funded in part by the Natural Sciences and Research Council of Canada (NSERC)

Questions?

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Conclusion

Video Sensors

Video sensors have distinct advantages: they are easy to install (or can be already installed) they are inexpensive they can provide rich traffic description (e.g. road user tracking) they can cover large areas their recording allows verification at a later stage

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Conclusion

Video-based System

Image Sequence + Camera Calibration

Road User Trajectories

Interactions

Applications

+

Labeled Images for Road User Type

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Motion patterns, volume, origin-destination counts, driver behavior

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Conclusion

Feature-based Road User Tracking in Video Data

Good enough for safety analysis and other applications, including the study of pedestrians and pedestrian-vehicle interactions [Saunier and Sayed, 2006] ´ N. Saunier, Polytechnique Montreal

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Conclusion

Motion Pattern Learning

Traffic Conflict Dataset, Vancouver 58 prototype trajectories (2941 trajectories)

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Reggio Calabria, Italy 58 prototype trajectories (138009 trajectories)

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Conclusion

´ C., editors (1977). Amundsen, F. and Hyden, Proceedings of the first workshop on traffic conflicts, Oslo, Norway. Institute of Transport Economics. Davis, G. A., Hourdos, J., and Xiong, H. (2008). Outline of causal theory of traffic conflicts and collisions. In Transportation Research Board Annual Meeting Compendium of Papers. 08-2431. Gettman, D. and Head, L. (2003). Surrogate safety measures from traffic simulation models, final report. Technical Report FHWA-RD-03-050, Federal Highway Administration. Ismail, K., Sayed, T., and Saunier, N. (2010a). Automated analysis of pedestrian-vehicle conflicts: Context for before-and-after studies. ´ N. Saunier, Polytechnique Montreal

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Conclusion

Transportation Research Record: Journal of the Transportation Research Board, 2198:52–64. presented at the 2010 Transportation Research Board Annual Meeting. Ismail, K., Sayed, T., and Saunier, N. (2010b). Camera calibration for urban traffic scenes: Practical issues and a robust approach. In Transportation Research Board Annual Meeting Compendium of Papers, Washington, D.C. 10-2715. Mohamed, M. G. and Saunier, N. (2013). Motion prediction methods for surrogate safety analysis. In Transportation Research Board Annual Meeting Compendium of Papers. 13-4647. Accepted for publication in Transportation Research Record: Journal of the Transportation Research Board. Saunier, N. and Mohamed, M. G. (2014). ´ N. Saunier, Polytechnique Montreal

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Conclusion

Clustering surrogate safety indicators to understand collision processes. In Transportation Research Board Annual Meeting Compendium of Papers. 14-2380. Saunier, N. and Sayed, T. (2006). A feature-based tracking algorithm for vehicles in intersections. ´ In Canadian Conference on Computer and Robot Vision, Quebec. IEEE. Saunier, N. and Sayed, T. (2008). A Probabilistic Framework for Automated Analysis of Exposure to Road Collisions. Transportation Research Record: Journal of the Transportation Research Board, 2083:96–104. presented at the 2008 Transportation Research Board Annual Meeting. Saunier, N., Sayed, T., and Ismail, K. (2010). ´ N. Saunier, Polytechnique Montreal

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Conclusion

Large scale automated analysis of vehicle interactions and collisions. Transportation Research Record: Journal of the Transportation Research Board, 2147:42–50. presented at the 2010 Transportation Research Board Annual Meeting. Saunier, N., Sayed, T., and Lim, C. (2007). Probabilistic Collision Prediction for Vision-Based Automated Road Safety Analysis. In The 10th International IEEE Conference on Intelligent Transportation Systems, pages 872–878, Seattle. IEEE. St-Aubin, P., Saunier, N., and Miranda-Moreno, L. F. (2014). Road user collision prediction using motion patterns applied to surrogate safety analysis. In Transportation Research Board Annual Meeting Compendium of Papers. 14-5363. ´ N. Saunier, Polytechnique Montreal

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Conclusion

Svensson, A. (1998). A Method for Analyzing the Traffic Process in a Safety Perspective. PhD thesis, University of Lund. Bulletin 166. ´ C. (2006). Svensson, A. and Hyden, Estimating the severity of safety related behaviour. Accident Analysis & Prevention, 38(2):379–385.

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