Automated Road Safety Analysis

and Data Collection Using

Video Sensors

Tarek Sayed

University of British Columbia

Acknowledgement

Nicolas Saunier (École Polytechnique de Montréal)

Karim Ismail (Carleton University)

Mohamed Zaki (UBC)

Jarvis Autey (UBC)

Greg Mori (SFU)

Engineering Approaches to Road

Safety Management

Reactive approach Making improvements to existing unsafe road locations based on accident history

Proactive approach Prevent unsafe road conditions from occurring by including road safety as a priority at the planning/ design and early operation stages

The earlier that road safety is considered, the more cost-effectively it can be accommodated

Motivation

Traditional road safety analysis is a reactive approach, based on historical collision data

There are well-recognized availability and quality problems associated with collision data Less complete understanding of the complex interaction of collision factors and how safety measures work A more proactive approach is needed which provides a better understanding of collision occurrence

Traffic Conflicts (near-misses)

Shortcomings Cost of data collection Issues related to the reliability and accuracy of human observers

Source: Hyden, 1987

A Modular System for Vision-based

Automated Road Safety Analysis

Motion PatternsVolume, Origin-

Destination CountsDriver Behavior...

Trajectory Database Interaction Database

Traffic Conflict DetectionExposure MeasuresInteracting Behavior...

Image Sequence

Interpretation Modules

(Saunier and Sayed, 2006)

Video Analysis

Real-world Coordinates Recovery

1 2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

1

2

3

4

5 6

7 8 9 1

0 11

12

13

14

15

16

17

18

19 2

0

21

22

(Ismail, Sayed and Saunier, 2009)

Recovery of Real-world

Coordinates

o Calgary Glenmore Trail & 5 Street

o Edmonton

Recovery of Real-world

Kuwait City

Kuwait City

Video Analysis

Example of Motion Patterns (Calgary)

Calgary Gilmore and No. 5

Video Analysis

Road User Classification

Oakland, CA Chinatown 2004

Oakland, CA Chinatown

(Ismail, Sayed and Saunier, 2009)

Video Analysis

Road User Classification

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% false positive rate (veh as ped)

% t

rue p

ositiv

e r

ate

(ped a

s p

ed)

Receiver operating characteristic curve for three classification schemes

Max speed classifier

Smoothed max speed classifier

Prototype classifier

Ismail, Sayed and Saunier (2010)

Video Analysis

Validation

Oakland, CA Chinatown 2004

Vancouver Fire Works

Vancouver Fire Works

(with Greg Mori SFU)

- - Canadian Conference on Computer and Robot Vision (CRV 2010)

Objective Conflict Indicator

Vehicle-vehicle Interactions

Ux is current track Hxy is extrapolation hypothesis CP

x is collision point

U1

U2

H11

H12

H21

H22

CP2

CP1

(Saunier and Sayed, 2008)

Results (Old Training Video)

Results

Conflict Indicators calculated for traffic event 1.

DST: Deceleration t(Ismail, Sayed and Saunier, 2010)o Safety Time, GT: Gap \time, TTC: Time to

Collision, PET: Post Encroachment Time

Automated Before-and-After Safety

Projects

1. Before/After Evaluation of Pedestrian Scramble (California)

2. Before/After Analysis for the Treatment at Yellowhead / Victoria Trail

Before/After Evaluation of Pedestrian

Scramble (Ismail, Sayed and Saunier, 2010)

Motion Patterns Pre-Scramble Post-Scramble

Results

Ismail, Sayed and Saunier (2010)

Before-and-After Conflict Indicators

0 2 4 6 8 10 12 14 16 18 200

100

200

300

400

500

600Histogram of Before-and-After TTC

TTCmin

in seconds

Fre

quency o

f tr

aff

ic e

vents

-10 -8 -6 -4 -2 0 2 4 6 8 100

2000

4000

6000

8000

10000

12000

DSTmax

in seconds

Fre

quency o

f tr

aff

ic e

vents

Histogram of Before-and-After DST

-20 -15 -10 -5 0 5 10 15 200

500

1000

1500

2000

2500

3000

PET in seconds

Fre

quency o

f tr

aff

ic e

vents

Histogram of Before-and-After PET

0 2 4 6 8 10 12 14 16 18 200

1000

2000

3000

4000

5000

|PET| in seconds

Fre

quency o

f tr

aff

ic e

vents

Histogram of Before-and-After |PET|

-5 0 5 10 15 200

1000

2000

3000

4000

GTmin

in seconds

Fre

quency o

f tr

aff

ic e

vents

Histogram of Before-and-After GT

0 2 4 6 8 10 12 14 16 18 200

1000

2000

3000

4000

|GTmin

|in seconds

Fre

quency o

f tr

aff

ic e

vents

Histogram of Before-and-After |GT|

TTC Before

TTC After

DST Before

DST After

PET Before

PET After

|PET| Before

|PET| After

GT Before

GT After

|GT| Before

|GT| After

Ismail, Sayed and Saunier (2010)

B/A Studies (Scramble Phase)

Before After

Ismail, Sayed and Saunier (2010)

EDMONTON B/A YELLOWHEAD

/ VICTORIA TRAIL RAMP

Motion Patterns

Motion Patterns

Camera Calibration (Before)

Conflict Examples (Before Data)

Conflict Examples (Before Data)

After Analysis

Conflict Examples (After Data)

Results

CONFLCIT TYPE

BEFORE

TREATMENT AFTER TREATMENT

B/A RATIO

Merging

1.3151e-004 4.5799e-006 28.71

1.1399e-004 4.1768e-006 27.29

9.1363e-005 3.4317e-006 26.62

Rear-end

2.8288e-005 2.6896e-005 1.05

2.8716e-005 2.3093e-005 1.24

2.4232e-005 1.6991e-005 1.43

All Types

9.1685e-005 7.6311e-006 12.01

8.1091e-005 6.7632e-006 11.99

6.5463e-005 5.2856e-006 12.39

Penticton, BC - Before/After

Analysis of Right-turn Treatment

Evaluating the Reliability of Red-

Light Cameras

Evaluating the Reliability of Speed

Cameras

Automated Safety Analysis -

Conclusion A new approach to road safety analysis

Proactive, generic and low cost approach

Provides better understanding of driver behavior especially collision avoidance mechanisms

Diagnostic approach

Overcomes the problems with the traffic conflict technique (high cost and reliability of observers)

It is time to take safety analysis in a new direction

Ongoing Studies /Future Work

Automatic detection of traffic violation events (TRB, 2011)

Several B/A studies using conflicts

Aggregation of conflict indicators (TRB, 2011)

Safety diagnosis of collision prone locations (roundabout)

Conflict/Collision models