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.
Kernelized Convolutional and Transformer Based Hierarchical Spatio-temporal Attention Network for Autonomous Vehicle Trajectory Prediction
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 23 , 2 ; 676-701
2025-08-01
26 pages
Article (Journal)
Electronic Resource
English
Trajectory prediction , Kervolution , Sequential network , Intelligent vehicle , Transformer , Autonomous vehicle Psychology , Automotive Engineering , Civil Engineering , Electrical Engineering , Information and Computing Sciences , Psychology and Cognitive Sciences , Engineering , Artificial Intelligence and Image Processing , User Interfaces and Human Computer Interaction , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics