## 1 Introduction  
Intelligent and connected vehicles (ICVs) are equipped with cameras, lidar, and other on-board sensors as well as controllers, actuators, and other devices, integrating modern network communication technology which can fulfil the functions of environment perception, intelligent decision making, and cooperative control [1]. As the bridge between environment perception and decision making, the accuracy and rationality of trajectory prediction results directly affect the rationality and safety of subsequent decision making and path planning of ICVs. However, due to the complex interactions among vehicles, the generation of accurate and efficient predictions with respect to the vehicle trajectories in the future has become a recognized problem and challenge in both academia and industry. In response to the above problem, researchers have conducted in-depth studies, and the existing vehicle trajectory prediction methods can be roughly grouped into four categories [2]: (1) physics-based methods; (2) classic machine-learning-based methods;(3) deep-learning-based methods; (4) reinforcement-learning-based methods. However, the physics-based vehicle trajectory prediction methods [3,4] are only applicable to short-term trajectory predictions with a prediction horizon of no more than 1s, and cannot effectively complete the medium- and long-term prediction tasks; classic machine-learning-based methods [5,6], on the other hand, are mostly only applicable to simpler driving scenes, and have poor trajectory prediction effects in complex driving scenes. Reinforcement-learning-based methods [7,8] require higher computational and time costs compared to other prediction methods. Vehicle trajectory prediction methods based on deep learning consider not only physics-related factors but also interaction-related factors and are able to adapt to more complex traffic scenes. Thus, deep-learning-based [9, 10, 11, 12] vehicle trajectory prediction methods have been gaining popularity in recent years. Early prediction models based on recurrent neural networks (RNNs) only use a single RNN module, as available literature [13] uses a module based on a three-layer long short-term memory (LSTM) stack to extract feature information for the prediction of the future trajectory of the vehicle. Subsequent scholars have mostly used multiple RNN modules with different functions. For example, the first LSTM-based module in the literature [14] was used to identify driving intentions, which were then fed into the second LSTM-based module along with vehicle longitudinal feature parameters for trajectory prediction. Inspired by the great success of convolutional neural networks (CNNs) in the field of computer vision in recent years, researchers have tried to apply CNNs to the field of vehicle trajectory prediction. CS-LSTM [15] introduces convolutional layers to replace the fully connected layers in the Social-LSTM network [16] to extract the vehicle spatial interaction information stored in the social tensor, which further improves the accuracy of prediction. The MANTRA algorithm [17] uses CNN-based memory enhancement networks to process historical trajectories and semantic map information to understand and learn scene pictures and scene features. The emergence of transformers (TFs) [18] has encouraged scholars to focus on attention mechanisms (AMs). The ST-attention module [19] introduces the AM into the prediction network to extract temporal affinity, i.e., the importance weights of historical trajectory information at different moments. Available literature [20] extracts vehicle and road information based on multi-head attention to output the probability distribution of future vehicle trajectories.