## 2 Related Work  
Conventional trajectory prediction methods mainly rely on kinematics and dynamics models. The constant velocity (CV), constant acceleration (CA), and their combinations were adopted in the early years of research [15]. [PERSON] et al. employed the Kalman filter for vehicle trajectory prediction [16]. Considering model nonlinearity, [PERSON] adopted the unscented Kalman filter in a constant-turn-rate model for better prediction performance [17]. Other scholars attempted to analyze vehicle behavioral characteristics from historical motion states. Inspired by the human driver's visual system, [PERSON] et al. implemented a mixed Gaussian model combined with a hidden Markov model to derive earlier lane-changing intentions of drivers by judging based on sudden changes in the speed of the target vehicle [18]. [PERSON] et al. exploited multiple predictive features including historical states of vehicles and the road structures, which were entered into a dynamic Bayesian network to infer the probability of each maneuver of the target vehicle [19]. Such methods are simple to compute. However, they mainly rely on the current motion information of the target vehicle. Model uncertainties and changes in the driver behavior are not covered. Furthermore, environmental interactions are also not considered. So, a physical model is only suitable for short-term prediction. Although some other approaches [20; 21; 22] integrated physical and behavioral models through multi-model interaction to enhance the prediction accuracy, this still has insufficient reliability for long-term prediction, especially in complex traffic environments. The essence of vehicle trajectory prediction is the regression of time-series data. LSTM has been subject to significant advances in sequence generation, meaning it has been widely adopted for natural language processing, target tracking, and trajectory prediction in recent years. [PERSON] proposed an LSTM-based trajectory aggregating method in which they synthesized all the information through a pooling operation and applied a decoder to generate predicted trajectories [7]. Other forms of LSTM incorporated with different networks have also been utilized to better represent multi-object interactions. [PERSON] incorporated the graph representation learning module into an LSTM encoder-decoder model in order for it to precisely learn the spatial interactions between vehicles [8]. [PERSON] processed spatio-temporal trajectory information through a combination of a GNN and CNN [14]. [PERSON] applied graph convolutional blocks to represent the interactions of close objects [23]. [PERSON] et al. established a multi-scale heterogeneous network for varying numbers of moving objects, and the prediction of targets was realized by decoding the hidden features [24]. [PERSON] et al. proposed an interaction-aware Kalman neural network (IaKNN)-based multi-layer architecture to resolve a high-dimensional traffic environment [25]. To capture the importance of spatio-temporal coupling information more accurately and further enhance the prediction performance, [PERSON] applied a multi-head attention mechanism that encodes the vehicle dynamics and category information, and they formulated a multimodal trajectory prediction framework [26]. [PERSON] presented a multi-vehicle trajectory prediction based on establishing a heterogeneous edge-enhanced graph attention network describing the multi-vehicle interaction mechanism and introducing a gate-based multi-objective selective map sharing mechanism [13]. To extract feature information more efficiently, [PERSON] introduced reinforcement learning into the graph attention mechanism in order to determine the relative significance of each node in continuous training interactions [27]. Some other forms of attention mechanisms from different perspectives have also been adopted in similar studies, which effectively improved the prediction performance [28; 29; 30].