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A hybrid data-driven and mechanism-based method for vehicle trajectory prediction

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Abstract

Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories. Existing approaches commonly employ an encoder–decoder neural network structure to enhance information extraction during the encoding phase. However, these methods often neglect the inclusion of road rule constraints during trajectory formulation in the decoding phase. This paper proposes a novel method that combines neural networks and rule-based constraints in the decoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules. The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder (CVAE) to capture trajectory uncertainty. The evaluation results demonstrate a reduction of 32.4% and 27.6% in the average displacement error (ADE) for predicting the top five and top ten trajectories, respectively, compared to the baseline method.

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Authors and Affiliations

  1. School of Automotive Studies, Tongji University, Shanghai, 201804, China

    Haoqi Hu, Bin Li, Lin Zhang & Yanjun Huang

  2. Dongfeng Motor Corporation, Wuhan, 430000, Hubei, China

    Xiangming Xiao & Zeyang Zhang

  3. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Hong Chen

Authors
  1. Haoqi Hu
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  2. Xiangming Xiao
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  3. Bin Li
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  4. Zeyang Zhang
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  5. Lin Zhang
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  6. Yanjun Huang
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  7. Hong Chen
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Corresponding author

Correspondence to Lin Zhang.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grant 52372393, 62003238, and in part by the DongfengTechnology Center (Research and Application of Next-Generation Low-Carbonntelligent Architecture Technology).

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Hu, H., Xiao, X., Li, B. et al. A hybrid data-driven and mechanism-based method for vehicle trajectory prediction. Control Theory Technol. 21, 301–314 (2023). https://doi.org/10.1007/s11768-023-00170-x

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  • DOI: https://doi.org/10.1007/s11768-023-00170-x

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