The ability to accurately predict feasible multi-modal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conventional dense future endpoints with a small set of fixed prior motion intention points. However, the fixed prior intention points make the MTR multi-modal prediction distribution over-scattered and infeasible in many scenarios. In this paper, we propose the ControlMTR framework to tackle the aforementioned issues by generating scene-compliant intention points and additionally predicting driving control commands, which are then converted into trajectories by a simple kinematic model with soft constraints. These control-generated trajectories will guide the directly predicted trajectories by an auxiliary loss function. Together with our proposed scene-compliant intention points, they can effectively restrict the prediction distribution within the road boundaries and suppress infeasible off-road predictions while enhancing prediction performance. Remarkably, without resorting to additional model ensemble techniques, our method surpasses the baseline MTR model across all performance metrics, achieving notable improvements of 5.22% in SoftmAP and a 4.15% reduction in MissRate. Our approach notably results in a 41.51% reduction in the cross-boundary rate of the MTR, effectively ensuring that the prediction distribution is confined within the drivable area.


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    Title :

    ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction


    Contributors:
    Sun, Jiawei (author) / Yuan, Chengran (author) / Sun, Shuo (author) / Wang, Shanze (author) / Han, Yuhang (author) / Ma, Shuailei (author) / Huang, Zefan (author) / Wong, Anthony (author) / Tee, Keng Peng (author) / Ang, Marcelo H. (author)


    Publication date :

    2024-09-24


    Size :

    3424389 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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