Motion planning directly in the spatiotemporal dimension can generate trajectories of higher quality compared to decoupled methods for autonomous driving. However, it requires a greater amount of computational resources. This paper proposes an efficient motion planning method based on convolution in the spatiotemporal dimension, which takes into account the uncertainty of localization and obstacle intention. Firstly, a three-dimensional probability occupancy grid map with uncertainty is constructed based on prediction results. Secondly, convolution kernels are generated considering the contour, heading angle and localization uncertainty of the ego vehicle. Thirdly, single-channel multi-output convolutions are performed between the probability occupancy grid map and the kernels to generate the four-dimensional feature map. Finally, a collision avoidance algorithm based on the feature map is proposed to obtain the optimal trajectory, which uses the hybrid A* algorithm. The chance constraint and the vehicle kinematics are taken into account in the motion planning. In simulation experiments, the safety performance, computational efficiency and rationality of the motion planning are compared and analyzed, and the proposed method performs superiorly. In addition, real-world experiments verify the feasibility of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Efficient Uncertainty-Aware Collision Avoidance for Autonomous Driving Using Convolutions


    Contributors:
    Zhang, Chaojie (author) / Wu, Xichao (author) / Wang, Jun (author) / Song, Mengxuan (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    6203850 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Perception for collision avoidance and autonomous driving

    Aufrere, R. / Gowdy, J. / Mertz, C. et al. | Tema Archive | 2003


    Intent-aware collision avoidance for autonomous marine vehicles

    Leavitt, Joseph William | DSpace@MIT | 2017

    Free access

    AUTONOMOUS DRIVING SYSTEM AND COLLISION AVOIDANCE METHOD USING SAME

    NAM SUNG KI / CHIRAKKAL VINJOHN VINCENT | European Patent Office | 2025

    Free access

    A prioritized collision avoidance methodology for autonomous driving

    Shahriari, Mohammadali / Biglarbegian, Mohammad | IEEE | 2016


    AUTONOMOUS DRIVING COLLISION AVOIDANCE APPARATUS AND METHOD THEREOF

    ZHOU YU | European Patent Office | 2022

    Free access