## 2 Related Work  
Earlier trajectory prediction research mainly focused on designing algorithms based on the principles of physical kinematics. [PERSON] et al. applied Kalman filtering and Monte Carlo method into trajectory prediction and achieved good results in short-distance prediction [5]. [PERSON] et al. established a vehicle motion model using polynomial planning and completed future trajectory prediction based on the principles of acceleration and constant yaw rate [6]. [PERSON] et al. proposed an improved road trajectory evaluation algorithm that used Monte Carlo simulations and improved dynamic models to identify threats in road scenes [7]. In the past decade, machine learning and related technologies have been widely applied in the field of vehicle trajectory prediction. And, compared to traditional physics-based model methods, prediction models based on machine learning have achieved significant breakthroughs in key indicators such as prediction accuracy. Classic machine learning methods and models such as support vector machine (SVM), multi-layer perceptron (MLP), and dynamic Bayesian network (DBN) were first applied in early trajectory prediction research. For example, [PERSON] et al. proposed a prediction algorithm based on an MLP model, but the algorithm could only predict discrete segments of trajectories rather than complete future vehicle trajectories [8]. [PERSON] et al. proposed a hybrid model based on physical vehicle models and operation recognition models in which the operation recognition model was implemented through hidden Markov models [9]. With the rapid development of deep learning, deep learning models that can better predict complex scenarios have gradually replaced classical machine models. Among the many deep learning models, recurrent neural networks (RNNs) [10; 11; 12] were originally proposed to solve sequence learning problems, and so are naturally advantageous for tasks like trajectory prediction. Long short-term memory (LSTM) [13; 14; 15; 16], building upon RNN models, incorporates a cell state representing long-term memory, and thus its ability to handle long-term dependencies is enhanced. Recent research is moving towards multi-modal trajectory prediction and exploring generative models. [PERSON] et al. addressed the multi-modal prediction problem by combining self-sequential prediction and GAN [3; 17]. [PERSON] incorporated the idea of CVAE into the LSTM prediction model, enabling the hybrid model to achieve multi-modal trajectory prediction [18]. [PERSON] et al. proposed Transformer [19], which can better handle long-range dependencies compared to RNN models. Therefore, many studies have also attempted to apply Transformer to vehicle trajectory prediction [20; 21; 22; 23]. In recent years, reinforcement learning has also been gradually applied to vehicle trajectory prediction research, in which a Markov decision process has been applied to a greater degree and has achieved good results [24]. With the continuous advancement of traffic information collection technology, most newer public datasets provide high-definition maps of predicted scenarios. How to obtain effectively characterized environmental feature information from high-definition maps and how to incorporate it into a model training process are the questions at the forefront of research. Early studies on environmental information processing mainly used convolutional neural networks (CNNs) to learn from grid maps of roads. [PERSON] et al. used two CNNs to encode information about the environment around a predicted object and combined reward graphs representing probability scores and direction information to derive the most likely path [25]. ChauffeurNet annotated original aerial maps of roads with lane lines, traffic lights, etc., to obtain a series of grid maps. These grid maps were input into a convolutional network to extract contextual features and shared with other network modules during training [26]. [PERSON] et al.