This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and RL policy, to ensure the safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviours of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, our proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs).


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    Titel :

    Eco-driving at signalized intersections: a parameterized reinforcement learning approach


    Beteiligte:
    Jiang, Xia (Autor:in) / Zhang, Jian (Autor:in) / Li, Dan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    31.12.2023


    Format / Umfang :

    26 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Signalized intersections

    Miller, A.J. | Engineering Index Backfile | 1968


    Minimizing energy consumption from connected signalized intersections by reinforcement learning

    Bin Al Islam, S.M.A. / Aziz, H M Abdul / Wang, Hong et al. | IEEE | 2018



    Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

    Yang, Hao / Almutairi, Fawaz / Rakha, Hesham | IEEE | 2021