Estimating Drivers’ Behaviors from Event Data Recorder Data

Thursday, March 22, 2018

About the Presentation

For at least 15 years, it has been recognized that pre-crash data captured by event data recorders (EDRs) might help illuminate the actions of drivers prior to crashes. In left-turning crashes where pre-crash data are available from both vehicles, it should be possible to estimate features such as the location and speed of the opposing vehicle at the time of turn initiation and the reaction time of the opposing driver. However, difficulties arise from measurement errors in pre-crash data and because the EDR data from the two vehicles are not synchronized, and the resulting uncertainties should be accounted for.

This presentation described a method for accomplishing this using Markov Chain Monte Carlo computation. First, planar impact methods were used to estimate the speeds at impact of the involved vehicles. Next, the impact speeds and pre-crash EDR data were used to reconstruct the vehicles’ trajectories during approximately five seconds preceding the crash. Interpolation of these trajectories was then used to estimate speeds and distances at critical times. The method was illustrated using several cases from the NASS/CDS database.

Webcast

About the Speaker

Gary Davis is a professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. His research interests include causal inference and impact assessment in traffic safety; the application of accident investigation and reconstruction methods to traffic engineering questions; the use of Bayesian statistical methods in traffic and transportation engineering; and the application of optimization methods to problems in traffic engineering and transportation planning.