Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective eval-uation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic


    Beteiligte:
    Zheng, Lei (Autor:in) / Yang, Rui (Autor:in) / Peng, Zengqi (Autor:in) / Liu, Haichao (Autor:in) / Wang, Michael Yu (Autor:in) / Ma, Jun (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    4544505 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real World Driving: Emissions in Highly Congested Traffic

    Khalfan, Ahmad / Andrews, Gordon / Li, Hu | SAE Technical Papers | 2017


    Real World Driving: Emissions in Highly Congested Traffic

    Khalfan, Ahmad / Andrews, Gordon / Li, Hu | British Library Conference Proceedings | 2017


    Pole-Based Real-Time Localization for Autonomous Driving in Congested Urban Scenarios

    Weng, Lihong / Yang, Ming / Guo, Lindong et al. | IEEE | 2018



    Control of Spatiotemporal Congested Traffic Patterns at Highway Bottlenecks

    Kerner, B. / IEEE | British Library Conference Proceedings | 2005