## 1 Introduction  
Currently, research on vehicle trajectory prediction, powered by artificial intelligence technology, has made significant progress. Video data collected from expressway monitoring cameras serve as a valuable resource for predicting vehicle trajectories. This facilitates applications such as collision warnings and other vehicle-road collaborative functionalities, effectively enhancing driving safety [1, 2].  
The actual trajectory of a vehicle is influenced by various factors such as road conditions, the positions of other vehicles, and the driving habits and intentions of the driver. Early vehicle trajectory prediction methods has thus far relied solely on historical data, using only a vehicle's past driving data to learn and predict a certain future trajectory. However, in reality, the behavior of drivers is influenced by their driving intentions. For example, different destinations can result in significantly different future trajectories. Therefore, inferring driving intentions is crucial in trajectory prediction. Recent research has proposed incorporating intent inference modules to prevent models from converging on conservative results [3, 4]. Instead of predicting a single outcome, the model generates multiple possible trajectory results based on potential driving intentions.  
However, current studies have not thoroughly considered the mechanisms through which road structure and traffic environment influence drivers' intentions. Most studies simply utilize global road structure and the spatial distribution of vehicles from a bird's-eye view as inputs to the prediction model. However, in reality, each driver observes different road information. They can only perceive the road structure and traffic environment from the perspective of the vehicle's current location, and plan their route accordingly.  
To address the above-mentioned issue, this paper proposes a novel trajectory prediction approach termed the CVAE-VGAE model. The model consists of a main model responsible for inferring driving destination intent and predicting trajectories, and a sub-model responsible for extracting road map information. The main model improves the encoding and decoding structure of the CVAE model by incorporating self-attention mechanisms. In order to enhance the model's perception of road environment data, a sub-model for road map information extraction and based on the VGAE is implemented. This sub-model reduces the dimensionality of graph-structured data and extracts environment feature variables. Unlike previous studies that have dealt with data from a global perspective, the sub-model in our paper will independently compute the spatial relationship between a vehicle and key points on the road from the perspective of each vehicle. This ensures that the final trajectory predictions fully consider the road information based on each driver's own perspective. Compared to models in previous studies, our model, by integrating environmental information, significantly improves the accuracy of simultaneous prediction of multiple vehicle trajectories in complex scenarios.  
Section 1 of this paper introduces the background of the research topic. It highlights the significance of the research based on the existing problems in the current studies and outlines the research objectives and content. Section 2 provides a summary of the relevant research work and the development process in this field. Section 3 focuses on the preprocessing of the dataset, with a particular emphasis on the comprehensive processing of map data. Section 4 describes the design of the model, which includes the main prediction model and the map processing sub-model. Section 5 analyzes the experimental data and presents the findings of this study. Finally, Section 6 concludes the paper and proposes directions for future improvements.