With autonomous vehicles (AVs) being actively developed, it becomes possible to optimize vehicle control policies and traffic management tools in a mixed vehicular environment. For individual AV control, acceleration and lane change are the two elementary driving behaviors that need to be coordinated to minimize disturbance to the entire traffic dynamics. In this paper, a joint decision policy of acceleration and lane change actions for AVs on a merging ramp is proposed and trained in a mixed autonomy traffic, using the technique of deep reinforcement learning. Our method is able to train policies that have limited impact on highway traffic while maintaining a relatively high merge throughput. We experimented with two reward functions, designed for the AV's selfish benefits and for the network traffic's social benefits. This paper then examines the emergent behaviors exhibited by the trained policies and their impacts on the main highway traffic at different density levels.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Developing a Merge Lane Change Decision Policy for Autonomous Vehicles by Deep Reinforcement Learning


    Contributors:


    Publication date :

    2021-09-19


    Size :

    3060547 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

    Nassef, Omar / Sequeira, Luis / Salam, Elias et al. | ArXiv | 2020

    Free access

    Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

    Nassef, Omar / Sequeira, Luis / Salam, Elias et al. | IEEE | 2020


    Autonomous Lane Change Decision Making Using Different Deep Reinforcement Learning Methods

    Feng, Xidong / Hu, Jianming / Huo, Yusen et al. | ASCE | 2019