Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow  
[PERSON]  
1 China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (X.H)  
[PERSON]  
1 China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (X.H)  
[PERSON]  
1 China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (X.H)  
[PERSON]  
1 China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (X.H)  
[PERSON]  
1 China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (X.H)  
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vehicle's motion trends. Such methods are suitable for some simple scenarios, but have poor long-term prediction performance when facing a complex environment. Thanks to the rapid development of deep learning in recent years, it now achieves excellent performance in the fields of object detection, pattern recognition, and behavioral prediction owing to its strong feature extraction capabilities. For vehicle trajectory prediction, since changes in the motion of vehicles or humans are continuous and reflect significant temporal coupling characteristics, related networks such as recurrent neural networks (RNNs) [5] and long short-term memory (LSTM) [6; 7; 8; 9] are commonly adopted to extract the temporal features of target trajectories. At the same time, with the continuous enrichment of the autonomous driving dataset [10; 11], its scene coverage has been gradually improved, which also provides a strong support for model training. Furthermore, benefiting nowadays from the decreasing cost of sensors and the gradual popularization of V2X technology, autonomous vehicles can obtain richer environmental information. In order to characterize potential spatio-temporal interactions in complex environments, encoding-decoding frameworks based on a variety of aggregation modes have been widely adopted, including the generative adversarial network (GAN) [12], graphic neural network (GNN) [13], and convolutional neural network (CNN) [14]. However, the vast number of spatio-temporal interactions contain a lot of redundant information. To more effectively extract important features from a large amount of spatio-temporal information, attention mechanisms based on encoding-decoding frameworks have become mainstream approaches in recent years. To improve predictive accuracy and interpretability, an encoding-decoding framework that incorporates spatio-temporal information, allowing for a more comprehensive extraction of potential interaction relationships between the target vehicle and surrounding vehicles, is proposed in this paper. As shown in Figure 1, our approach contains three modules. The vehicle-vehicle interaction module embeds vehicle motion states via a graph message and extracts spatial interaction features using a graph attention mechanism (GAT). The historical trajectory encoder adopts BiLSTM to extract temporal trajectory features and combines them with vehicle-vehicle interaction coefficients to carry out spatio-temporal information aggregation. The decoder module finally generates the predicted trajectory by decoding the hidden variables. The remainder of the paper proceeds as follows. Section 2 is the literature review of the related work. Section 3 gives the description of the vehicle trajectory problem and the definition of the basic scenario parameters.