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

Paper #:
  • 2017-01-1411

Published:
  • 2017-03-28
Abstract:
For at least 15 years it has been recognized that pre-crash data captured by event data recorders (EDR) might help illuminate the actions taken by drivers prior to a crash. 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 speed data and because the EDR data from the two vehicles are not synchronized; 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 the approximately 5 seconds preceding the crash. Interpolation into these trajectories is then used to estimate speeds and distances at critical times. The methods are illustrated using data from two staged collisions and then applied to several cases from the NASS/CDS database. Example results, from NASS/CDS Case 2012-12-044, are shown below. Variable Posterior Mean Posterior Standard Deviation Opposing Vehicle Speed 65.0 ft/sec 1.4 ft/sec Opposing Vehicle Distance 170.1 feet 26.6 feet Opposing Vehicle Time Gap 2.6 seconds 0.43 seconds Opposing Driver Reaction Time 1.1 seconds 0.53 seconds
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