## 3 Description of the Dataset and Data Processing  
The final prediction performance of a vehicle trajectory prediction model is heavily influenced by the quality of a dataset. In order to achieve simultaneous prediction for all vehicles in the same scene, the required dataset should include complete trajectory time series data for all vehicles in specific road scenarios. Additionally, the road scenarios should be rich enough and encompass different types of road conditions, so that the trained model can have good prediction capabilities in various complex scenarios. Furthermore, in order to achieve the research goal of integrating road environment information during the training process, the dataset should be equipped with high-precision road maps for each scene and complete annotations of key road elements in the scene such as lane lines. Argoverse 2 is a collection of open-source autonomous driving data and high-definition maps sourced from six cities in the United States [30]. This paper focuses on using the motion prediction dataset in Argoverse 2. This dataset has been improved based on the dataset in Argoverse 1, and it includes 250,000 scenes with trajectory data for multiple vehicles. Each scene lasts for 11 s with a sampling frequency of 10 Hz. Scene data includes the center and heading of each tracked object in a 2D aerial view. A key feature of this dataset is that each scene is paired with a local map. This map contains rich geometric and semantic metadata to better understand the 3D scene, such as lane boundaries, drivable area polygons, and intersection annotations, aligning with the research objectives of this paper. The acquired public dataset cannot be directly used for model training, and needs to undergo some data preprocessing work. Raw data contains attributes unrelated to vehicle driving, such as the city of origin, and also records trajectories of other types of entities like pedestrians and motorcycles. Firstly, attribute filtering work is performed on the dataset to retain only the valid information related to a vehicle's trajectory. The retained attributes are the coordinates representing the current position of a vehicle, the velocity in the direction of the coordinate axis, the angle of orientation, the corresponding vehicle ID and the scene ID. The first 5 s of each trajectory record are separated off as inputs for the model (x), while the following 6 s are separated off as content to be predicted (y). As some vehicle trajectories in the scene may not completely cover the first 5 s, data cleaning is performed on these incomplete data. Finally, the dataset is augmented through two methods: translation augmentation and reverse trajectory augmentation. In addition to the vehicle trajectory data, this paper also extensively processed the map data provided by Argoverse 2 in JSON format. Since the map data files are complex, and the amount of environmental information input into the deep learning model should not be excessive, it is necessary to select the most relevant information. The map data provided by Argoverse 2 includes three types of data most relevant to highway scenes: lane centerlines, left lane boundaries, and right lane boundaries. This dataset provides sets of points for these boundaries and records the two-dimensional coordinates of these points, as shown in Figure 1. The lane centerline best represents the complete topological information of the road where the vehicle is located, while the left and right lane boundaries emphasize changes in the road during abrupt behavior such as turning. Figure 1: Display of the points in the map data of a scene from the Argoverse 2 dataset in plan view according to their coordinates. From (**left**) to (**right**) are the lane centerline, left boundary, and right boundary. Extracted point collection will go through the data processing shown in Figure 2 below and finally generate inputs based on the VGAE model. The first step is the filtering of key points of the road.