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An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network

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  • Shi, Kunsong
  • Wu, Yuankai
  • Shi, Haotian
  • Zhou, Yang
  • Ran, Bin

Abstract

Vehicle trajectory prediction is essential for the operation safety and control efficiency of automated driving. Prevailing studies predict car following and lane change processes in a separate manner, ignoring the dependencies of these two behaviors. To remedy this issue, this paper proposes an integrated deep learning-based two-dimension trajectory prediction model that can predict combined behaviors. Specifically, we designed a switch neural network structure based on the attention mechanism, bi-directional long-short term memory (BiLSTM) and Temporal convolution neural network (TCN) to mimic and predict the joint behaviors. Experiments are conducted based on the Next Generation Simulation (NGSIM) dataset to validate the effectiveness of our proposed model. As results indicate, our proposed model outperforms the state-of-art trajectory prediction models and can provide accurate short-term and long-term predictions.

Suggested Citation

  • Shi, Kunsong & Wu, Yuankai & Shi, Haotian & Zhou, Yang & Ran, Bin, 2022. "An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    • Handle: RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122002503
    DOI: 10.1016/j.physa.2022.127303
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    References listed on IDEAS

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    Cited by:

    1. Xu, Xinpeng & Yang, Chen & Wu, Weiguo, 2024. "Representation learning and Graph Convolutional Networks for short-term vehicle trajectory prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. Wang, Zhangu & Guan, Changming & Zhao, Ziliang & Zhao, Jun & Qi, Chen & Hui, Zilaing, 2024. "Expressway lane change strategy of autonomous driving based on prior knowledge and data-driven," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    3. Li, Gen & Zhao, Le & Tang, Wenyun & Wu, Lan & Ren, Jiaolong, 2023. "Modeling and analysis of mandatory lane-changing behavior considering heterogeneity in means and variances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    4. Khelfa, Basma & Ba, Ibrahima & Tordeux, Antoine, 2023. "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).

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