Autonomous driving is an important development direction of automobile technology, and driving strategy is the core of the autonomous driving system. Most works in this area focus on single-objective tasks, such as maximizing vehicle speed or lane-keeping, and rare attention has been paid to the quality of driving skills. Therefore, a multi-objective learning method is proposed for autonomous driving strategy based on deep Q-network, where two optimization objectives are involved, i.e., vehicle speed and passenger comfort. An end-to-end autonomous driving model is designed by using vehicle front camera images as inputs to the Q-network and makes decisions based on the output Q values. Considering the vehicle speed and passenger comfort, the reward function is designed for multi-objective optimization. To evaluate the effectiveness of the method, training and testing are performed in a simulator, and a single-objective strategy with the goal of maximizing speed is designed for comparison. The results show that the proposed multi-objective autonomous driving strategy can strike a balance between vehicle speed and passenger comfort. Compared with the single-objective strategy, the multi-objective strategy has a significant improvement in comfort, while the average speed is only slightly reduced.


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    Title :

    Multi-objective optimization for autonomous driving strategy based on Deep Q Network


    Additional title:

    Discov Artif Intell


    Contributors:
    Hu, Tianmeng (author) / Luo, Biao (author) / Yang, Chunhua (author)


    Publication date :

    2021-10-18




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English