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.


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    Titel :

    Autonomous Vehicle Path Planning Strategy Considering Uncertainty in Environmental Perception


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Limin (Herausgeber:in) / Yao, Dechen (Herausgeber:in) / Ma, Feng (Herausgeber:in) / Zhang, Liguo (Herausgeber:in) / Chen, Yuejian (Herausgeber:in) / Xue, Qingwan (Herausgeber:in) / Yuan, Mingfang (Autor:in) / Chen, Xumei (Autor:in) / Guo, Chen (Autor:in) / Zhang, Xinyuan (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    28.03.2025


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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