Vision-centric motion prediction concentrates on accurately determining the instance mask and its future trajectory from surround-view cameras, which manifests inherent merits such as holistic perspective and fully-differentiable spirit. Nonetheless, it is still impeded by sparse bird’s-eye view (BEV) representation and unfavorable temporal context across frames, resulting in a sub-optimal solution to decision-making and vehicle navigation. In this work, we propose a novel Difference-guide Motion Prediction for vision-centric autonomous driving, that is DMP, where it integrates BEV map refinement with spatial-temporal relation modeling in a hierarchical manner. Specifically, a bidirectional view projection strategy is introduced for the complementary BEV feature generation via depth-consistency correction. To promote spatiotemporal context aggregation, we design a difference-guided motion approach by offset approximation to align motion-aware cues between adjacent frames, and a dual-stream pyramid module is further developed for historical information fusion and future instance segmentation during specific durations. Extensive experiments on the large-scale nuScenes dataset demonstrate that it outperforms the baselines by a remarkable margin and delivers competitive motion prediction across diverse scenarios and range settings, suggesting its effectiveness and superiority. The details will be available at https://github.com/pupu-chenyanyan/DMP-VAD.


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    Title :

    DMP: Difference-Guided Motion Prediction for Vision-Centric Autonomous Driving


    Contributors:
    Chen, Yanyan (author) / Lin, Chunmian (author) / Duan, Xuting (author) / Zhou, Jianshan (author) / Guo, Kan (author) / Zhao, Dezong (author) / Cao, Dongpu (author) / Tian, Daxin (author)


    Publication date :

    2025-06-01


    Size :

    3622349 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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