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.


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    Title :

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


    Contributors:


    Publication date :

    2012-09-01


    Size :

    635404 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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