Using Extreme Value Theory for the Prediction of Head-On Collisions During Passing Maneuvres

This paper tests the Generalized Extreme Value (GEV) distribution as an EV method using the minimum time-to-collision with the opposing vehicle during passing maneuvers. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment were used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary base. Raw data were collected at a resolution of 0.1 s and included the longitudinal and lateral position, speed and acceleration of all vehicles in the scenario. From this raw data, the minimum time-to-collision with the opposing vehicle at the end of the passing, maneuver was calculated. GEV distribution based on the Block Maxima approach was tested for the estimation of head-on collision probabilities in passing maneuvers. The estimation results achieved good fit with respect to head-on collisions' prediction indicating that this is a promising approach for safety evaluation.