Intersection is one of the most accident-prone urban scenarios for autonomous driving wherein making safe and computationally efficient decisions is non-trivial. Current research mainly focuses on the simplified traffic conditions while ignoring the existence of mixed traffic flows, i.e., vehicles, cyclists and pedestrians. For urban roads, different participants lead to a quite dynamic and complex interaction, posing great difficulty to learn an intelligent policy. This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows. Specially, this representation introduces an encoding function and summation operator to construct driving states from environmental observation, capable of dealing with different types and variant number of traffic participants. A constrained optimal control problem is built wherein the objective involves tracking performance and the constraints for different participants, roads and signal lights are designed respectively to assure safety. We solve this problem by gradient-based optimization, wherein the reasonable state will be given by the encoding function and then served as the input of policy and value function. An off-policy training is designed to reuse observations from driving environment and backpropagation through time is utilized to update the policy function and encoding function jointly. Verification result shows that the dynamic permutation state representation can enhance the driving performance of IDC, including comfort, decision compliance and safety with a large margin. The trained driving policy can realize efficient and smooth passing in the complex intersection, guaranteeing driving intelligence and safety simultaneously.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections


    Beteiligte:
    Ren, Yangang (Autor:in) / Jiang, Jianhua (Autor:in) / Zhan, Guojian (Autor:in) / Li, Shengbo Eben (Autor:in) / Chen, Chen (Autor:in) / Li, Keqiang (Autor:in) / Duan, Jingliang (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-12-01


    Format / Umfang :

    7138038 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Signal control optimization for automated vehicles at isolated signalized intersections

    Li, Zhuofei / Elefteriadou, Lily / Ranka, Sanjay | Elsevier | 2014



    Modeling Vehicles Movement at Signalized Intersections

    Chen, Zhenqi / Mao, Baohua / Liu, Mingjun et al. | ASCE | 2008


    Optimizing Signalized Intersections Performance Under Conventional and Automated Vehicles Traffic

    Pourmehrab, Mahmoud / Elefteriadou, Lily / Ranka, Sanjay et al. | IEEE | 2020