This paper studies the decision making problem of autonomous vehicles in traffic. We model the interaction between an autonomous vehicle and the environment as a stochastic Markov decision process (MDP) and consider the driving style of an experienced driver as the target to be learned. The road geometry is taken into consideration in the MDP model in order to incorporate more diverse driving styles. By designing the reward function of the MDP, the desired, driving behavior of the autonomous vehicle is obtained using reinforcement learning. Simulated results demonstrate the desired driving behaviors of an autonomous vehicle.


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    Title :

    Highway Traffic Modeling and Decision Making for Autonomous Vehicle Using Reinforcement Learning


    Contributors:


    Publication date :

    2018-06-01


    Size :

    1392168 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English