## 1 Introduction  
In recent years, autonomous driving has become an important topic in research, and many studies exist on positioning [1], perception [2], decision-making [3, 4], motion planning [5, 6], and control [7, 8]. Among these, decision-making in autonomous driving is closely related to the operating conditions of other vehicles on the road, and road facilities can capture these operating conditions (including position, speed, pavement condition, etc.). Therefore, the coordination and cooperation of road facilities, autonomous driving, and traffic control have become important research topics [9, 10]. An important topic in vehicle-infrastructure cooperation is the extraction of vehicle trajectories. Continuous vehicle trajectory information can play an important role in autonomous driving and intelligent traffic control [11]. It can provide the trajectory position of other vehicles for autonomous vehicles, which could help autonomous vehicles judge the current and future trajectories of surrounding vehicles and determine the next action. Meanwhile, continuous vehicle trajectory data can assist intelligent traffic control, risk assessment, and early warnings and help traffic personnel analyze road operation conditions [12]. Currently, extracting continuous vehicle trajectory information is challenging [13]. Video sensors are used in most regions to obtain motion information, such as the location and speed of road traffic participants. However, the video sensor data used today are strongly affected by occlusion and perspective [14]; some have low positioning accuracy, and the detection results are strongly affected by the environment. Additionally, common sensor data problems exist, such as different IDs marking in different sensors, which prevents direct identification of the same vehicle by different sensors. Therefore, it is difficult to directly obtain continuous vehicle trajectories from the video data. Many studies have aimed to extract continuous vehicle trajectories from dynamic and static sensors [15; 16]. This task has several directions in research. Some studies concern extracting vehicle trajectories from single roadside visual sensing data, whereas others concern finding vehicles with the same ID in different sensor data. Some studies also concern predicting and completing vehicle trajectories. Although these studies solve the corresponding problems effectively, few have proposed an entire continuous vehicle trajectory reconstruction framework, and some did not consider the particularity of the traffic scene. When special problems occur (such as multiple similar vehicles appearing on the road simultaneously), the model cannot provide high accuracy. Therefore, it is essential to study the actual traffic scene and propose the corresponding vehicle trajectory reconstruction framework. In this research, we choose the expressway as the actual traffic scene and use the visual sensor data to construct the continuous vehicle trajectory. Constructing the vehicle trajectory on the expressway involves many problems. Firstly, the distance between the sensors on the expressway is long, resulting in gaps in the detected vehicle trajectory. Secondly, similar vehicles sometimes appear simultaneously on the expressway, making it difficult to distinguish them. To solve the above problems, we propose the corresponding reconstruction framework. The framework comprises three parts: single-region video trajectory extraction, multi-camera cross-region trajectory splicing, and vehicle trajectory completion. The framework first obtains a vehicle trajectory in a single region through video data. It then uses vehicle re-identification technology to match vehicle trajectories between different regions and splices the trajectories belonging to the same vehicle. After trajectory splicing, the framework uses a trajectory completion model based on graph attention to complete the missing parts of the vehicle trajectories.