Bayesian Estimation of Drivers’ Gap Selections and Reaction Times in Left-Turning Crashes from Event Data Recorder Pre-Crash Data

For at least 15 years it has been recognized that pre-crash data captured by event data recorders 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. Difficulties arise however from measurement errors in pre-crash data and because the EDR data from the two vehicles are not synchronized so the resulting uncertainties should be accounted for. This paper describes a method for accomplishing this using Markov Chain Monte Carlo computation. First, planar impact methods are used to estimate the speeds at impact of the involved vehicles. Next, the impact speeds and pre-crash EDR data are used to reconstruct the vehicles’ trajectories during approximately 5 seconds preceding the crash. Interpolation of these trajectories is then used to estimate speeds and distances at critical times. The method is illustrated using three cases from the NASS/CDS database.