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<p>In autonomous driving, predicting the future trajectories of adjacent traffic participants is critical for safe motion planning, navigating through challenging traffic environments, and improving safety. This work introduces a novel spatio-temporal attention-based model to predict the long-term trajectory and behaviour of a target vehicle over a long time horizon. The model employs a multi-head attention mechanism inspired by human reasoning, a Temporal Convolution Network (TCN) to establish temporal correlation in terms of motion state, and a kernel technique to construct a highly non-linear correlation for each vehicle in the driving scenario. Finally, a Transformer-based decoder is utilized to predict the target vehicle’s long-term trajectory using an encoded spatio-temporal environment. The experimental findings using the NGSIM dataset demonstrate that the suggested model performs better than the leading spatio-temporal networks, achieving a 12% reduction in Root Mean Square Error (RMSE) when predicting a trajectory lasting 5&#xa0;s. Additionally, the study analyzes the effectiveness of the proposed model by evaluating the effect of driving behaviour.</p>

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Kernelized Convolutional and Transformer Based Hierarchical Spatio-temporal Attention Network for Autonomous Vehicle Trajectory Prediction

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Omveer Sharma,
  • N. C. Sahoo,
  • N. B. Puhan

摘要

In autonomous driving, predicting the future trajectories of adjacent traffic participants is critical for safe motion planning, navigating through challenging traffic environments, and improving safety. This work introduces a novel spatio-temporal attention-based model to predict the long-term trajectory and behaviour of a target vehicle over a long time horizon. The model employs a multi-head attention mechanism inspired by human reasoning, a Temporal Convolution Network (TCN) to establish temporal correlation in terms of motion state, and a kernel technique to construct a highly non-linear correlation for each vehicle in the driving scenario. Finally, a Transformer-based decoder is utilized to predict the target vehicle’s long-term trajectory using an encoded spatio-temporal environment. The experimental findings using the NGSIM dataset demonstrate that the suggested model performs better than the leading spatio-temporal networks, achieving a 12% reduction in Root Mean Square Error (RMSE) when predicting a trajectory lasting 5 s. Additionally, the study analyzes the effectiveness of the proposed model by evaluating the effect of driving behaviour.