Analysis and prediction of deceleration behavior during car following using stochastic driver-behavior model

Driver deceleration behavior contains large amount of information regarding individual driving characteristics, driving environment, and situations perceived as potentially hazardous by a driver. This paper focuses on deceleration behavior involving both release of the gas pedal and depression of the brake pedal during on-the-road car following. A Bayesian framework was employed to calculate the probability of a driver decelerating at a given point in time, using only low-level driving signals. A stochastic driver-behavior model based on a Dirichlet process mixture model was employed to capture distinct characteristics of different driver´s driving behavior. In addition, this framework exploits estimated time-to-collision (TTC) information, using both negative and positive values as a criticality indicator of driving situations perceived by the driver. Experimental validation was conducted using the on-the-road car-following behavior of sixty-four drivers. The results showed the promise of this framework for estimating deceleration probability during car following.