Recognizing safety-critical events from naturalistic driving data

Abstract

New trends in research on traffic accidents involve conducting Naturalistic Driving Studies (NDS). NDS are based on large-scale data collection of driver, vehicle and environment information in real-traffic. NDS provide large data sets which have proven to be extremely valuable for the analysis of safety-critical events such as near crashes and incidents. NDS data needs to be filtered to recognize safety-critical events. Filtering safety-critical events has been traditionally achieved by using kinematics triggers (e.g. searching for deceleration below a certain threshold signifying harsh braking). The low sensitivity and specificity of this filtering procedure, however, requires manual annotation of video data to decide whether the events individuated by the triggers are actually safety-critical. Such reviewing procedure is based on subjective decisions, time-consuming, and often tedious for the analysts. This project looked into improving this reviewing procedure using video data collected from 100 Volvo cars during one year in Gothenburg within a NDS called euroFOT. More than 400 videos from the triggered events have been reviewed, concluding that driver’s reaction may be the key to discriminate safety-critical events. In fact, whether an event if safety-critical or not depends on the driver. Several statistical procedures have been then applied to automatically recognize driver reaction from video data. In this project, we showed how combining automated video analysis with kinematics triggers increases sensitivity of near crash recognition from NDS data. These results open up to new ways to use video frames in NDS.