Performing safe trajectory planning that matches perception capabilities is critical for autonomous vehicles (AV). It remains a challenge to handle uncertainty including the epistemic the aleatoric uncertainty in environmental perception in order to plan safe and accurate trajectories. We propose an integrated perception-prediction-planning algorithm for autonomous vehicles that quantifies and transfers DL-based perception uncertainties during prediction and performs prediction evaluation with uncertainty. The novelties of the approach are: 1) quantifying and transferring perceptual uncertainty to the downstream planning decision phase, which is partially extended using quantified uncertainty incorporated into a Rapidly-exploring Random Tree; 2) combining uncertainty analysis with an implicit scenario context-aware trajectory prediction framework that utilizes perceptual uncertainty as part of the implicit scenario context information; 3) integrating the proposed uncertainty-environment-aware trajectory predictor with a planning-based feasible candidate trajectory generator to capture dynamically changing perceptual states and output accurate predictions. Experimental results based on a driving behavioral dataset show that the proposed method further reduces the detour proportion of the path while ensuring safety.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous Vehicle Path Planning Strategy Considering Uncertainty in Environmental Perception


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Yao, Dechen (editor) / Ma, Feng (editor) / Zhang, Liguo (editor) / Chen, Yuejian (editor) / Xue, Qingwan (editor) / Yuan, Mingfang (author) / Chen, Xumei (author) / Guo, Chen (author) / Zhang, Xinyuan (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-03-28


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Autonomous Vehicle Path Planning Considering Dwarf or Negative Obstacles

    Yang, Lei / Wang, Qi / Tan, Yingqi et al. | IEEE | 2019


    AUTONOMOUS VEHICLE PATH PLANNING CONSIDERING DWARF OR NEGATIVE OBSTACLES

    Yang, Lei / Wang, Qi / Tan, Yingqi et al. | British Library Conference Proceedings | 2019



    Path Tracking Control of Autonomous Vehicle on Curved Road Considering Multi-Source Uncertainty

    Zhang, Liang / Zhao, Shuen / Chen, Wenbin et al. | SAE Technical Papers | 2021


    Path Tracking Control of Autonomous Vehicle on Curved Road Considering Multi-Source Uncertainty

    Chen, Wenbin / Zhao, Shuen / Zhang, Liang et al. | British Library Conference Proceedings | 2021