Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM)
Journal article, 2013

To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.



Advanced driver assistance system



Driver behavior


Azra Habibovic

SAFER, The Vehicle and Traffic Safety Centre

Chalmers, Applied Mechanics, Vehicle Safety

Emma Tivesten

Chalmers, Applied Mechanics, Vehicle Safety

Uchida Nobuyuki

Japan Automobile Research Institute

Jonas Bärgman

Chalmers, Applied Mechanics, Vehicle Safety

Mikael Ljung Aust

Chalmers, Applied Mechanics, Vehicle Safety

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 50 554-565

Areas of Advance


Subject Categories

Applied Psychology

Vehicle Engineering



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