Impact of driving context on stochastic driver-behavior model: Quantitative analysis of car following task

Driving context plays an essential role in driving behavior and driving performance of a driver. The contextual information surrounding a driving activity involves several factors and dimensions that influence a driver's behavior. However, a driver may or may not need to adopt a particular driving pattern for every distinct driving context conditions. In this paper, we statistically investigate the impact of various driving context conditions on the behavior prediction and context recognition performance of stochastic driver-behavior models. We employed a Dirichlet process mixture modeling framework to capture the underlying distributions of observed driving parameters under different driving context conditions. Experimental validation was conducted using the on-the-road car-following behavior of sixty-four drivers. The results showed that under two particular context conditions, drivers demonstrated distinct driving characteristics that could be efficiently recognized by stochastic driver-behavior models, and yet, some context-specific models could be exploited to predict driving behavior in other driving contexts.