Trajectory prediction plays a crucial role in bridging the gap between perception and planning in autonomous driving systems. However, most existing methods perform motion forecasting directly in the coupled spatiotemporal space but disregard a more fundamental and faithful interpretation of path intentions. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and goal intentions through a hierarchical lateral and longitudinal decomposition. For path selection, we propose a hybrid lateral predictor to choose fixed-distance lateral paths from a candidate set of map-based road-following paths and cluster-based free-move paths. For goal selection, we propose a lateral-conditional longitudinal predictor to choose plausible goals by sampling from the selected lateral paths. Finally, we incorporate lateral-longitudinal information to generate final future trajectories based on a category distribution of path-goal intentions. Experimental results demonstrate that PiH achieves competitive and well-balanced performance compared to state-of-the-art methods on both the Argoverse and the Waymo Open Motion Dataset.


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    Title :

    Planning-Inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving


    Contributors:
    Li, Ding (author) / Zhang, Qichao (author) / Xia, Zhongpu (author) / Zheng, Yupeng (author) / Zhang, Kuan (author) / Yi, Menglong (author) / Jin, Wenda (author) / Zhao, Dongbin (author)

    Published in:

    Publication date :

    2024-01-01


    Size :

    2707703 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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