Vehicle Trajectory Prediction Based on Spatial-temporal Attention Mechanism

Vehicle movements can be highly random, and the driving style used becomes complex in urban traffic environments. To overcome the difficulties in accurately predicting vehicle trajectory in complex traffic environments, the Social Generation Adversarial Network (Social GAN) machine-learning model was used to develop a vehicle trajectory prediction algorithm named SIA-GAN. This developed algorithm was based on a spatial-temporal attention mechanism by considering a vehicle's speed, acceleration, course angle driving state, and shape size, and an interaction influence force field between the different vehicles was derived. Based on the magnitude of the interaction influence force that characterized each vehicle at the scene, different spatial attention weighing factors were assigned to the vehicles, along with a component of stressed "attention" that incorporated the information of vehicles having a greater impact on each other's driving pattern. The time attention mechanism was then combined to mine the time dependence of the vehicle under consideration in terms of the trajectory's feature vector during the observation period. To verify its effectiveness, the proposed algorithm was iteratively trained on an open-source dataset and compared with three trajectory prediction algorithms (long short-term memory (LSTM), Social LSTM, and Social GAN). The results show that SIA-GAN not only improves the convergence speed during training but also significantly reduces the average displacement error (ADE), final displacement error (FDE), average velocity error (AVE), and average course angle error (ACAE) when compared with other existing algorithms for trajectory prediction. At predicts 3.2 s, each of the aforementioned indexes decreases by 51.25%, 60.1%, 37.84%, and 13.75%, respectively, on average. The average reduction at predicts 4.8 s is 52.78%, 61.47%, 35.92%, and 9.57%, respectively. Thus, the proposed SIA-GAN trajectory prediction algorithm can accurately and effectively reflect complex spatial interaction characteristics between vehicles, enhancing the accuracy, rationality, and interpretability of trajectory predictions. © 2023 Xi'an Highway University. All rights reserved.