The increasing number of motor vehicles promotes the consumption of transportation energy, which brings about a new research field named eco-driving. As a complex problem, the eco-driving operation near intersection is hard to be implemented. This paper utilizes the advanced reinforcement learning (RL) technique to solve the problem. A reward function that integrated safety, efficiency and energy conservation is designed, while a proximal policy optimization (PPO) algorithm is given to solve the continuous action space problem. Microscopic traffic simulations show that the smart electric vehicle can learn a speed profile that avoids unnecessary stopping near the intersection. Meanwhile, the proposed framework can significantly reduce the energy consumption of the electric vehicles by more than 66% with less increase of vehicle travel time. Moreover, sensitivity analysis shows that the trade-off between energy consumption and travel time can be flexibly adjusted by setting the parameters.


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    Titel :

    Eco-driving for Intelligent Electric Vehicles at Signalized Intersection: A Proximal Policy Optimization Approach


    Beteiligte:
    Zhang, Xiaoliang (Autor:in) / Jiang, Xia (Autor:in) / Li, Nan (Autor:in) / Yang, Zhongyue (Autor:in) / Xiong, Zhuang (Autor:in) / Zhang, Jian (Autor:in)

    Kongress:

    ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation ; 2021 ; Xishuangbanna, China


    Erschienen in:

    Erscheinungsdatum :

    2022-01-01


    Format / Umfang :

    7 pages



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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