## 2 Related Work  
In terms of lane changing, the earliest research studied the construction of the lane-changing decision model. [PERSON] [4] first proposed a relatively complete lane-changing model that considers the front and rear vehicle speed, acceleration and distance status, which can simulate the lane-changing behavior on urban roads. However, the deterministic method fails to describe the random lane-changing performance of drivers. At the same time, the estimates of acceptable lane changes are conservative. The category of a discrete lane-changing choice model was generated and developed based on measured traffic flow data of California I-80 freeway obtained by FHWA's NGSIM project [5; 6]. However, the structure and parameters of the model are complex. The MOBIL model intuitively represents the lane choice utility using acceleration as the main indicator, which is capable of comprehensively considering the distribution weight of individual and collective interests [7]. Scholars have also tried to model and analyze the parameters active during the lane-changing process from the aspect of the actual explanation for lane-changing behavior. [PERSON] et al. [8] extracted forced and arbitrary lane-changing events, analyzed different lane-changing behaviors of heavy vehicle drivers, and investigated changes in the lane-changing gap, acceptance, and duration. The results showed that forced lane changes are more positive than arbitrary lane changes in terms of acceptance and lane change execution. [PERSON] et al. [9] studied the statistical properties of lane change parameters based on NGSIM data. Through these parameters describing vehicle gap, collision time, and speed of the target vehicle, the interaction between lane-changing vehicle and surrounding vehicles was analyzed, and the distributions of parameters were obtained. [PERSON] et al. [10] considered the driver's visual searching behavior (driver's line of sight declination), vehicle operation behavior (whether to prompt steering), vehicle motion state (vehicle speed), and driving conditions (spacing, TTC indicators, etc.) and built a lane change prediction index system. A back-propagation neural network model was developed to predict lane-changing behavior. Using an instrumented test vehicle approach to collect data along selected road segments, [PERSON] et al. [11] concluded that most drivers accept short gaps when performing lane changes, and the spacing is considered a more important variable than headway. Drivers are often guided by spatial rather than temporal consideration when driving at a desired speed. [PERSON] et al. [12] shifted the research perspective of lane changing from microscopic to macroscopic, studying the relationship between the lane change spacing provided by the off-ramp facility and the traffic flow condition. The VISSIM-based micro-behavioral simulation obtains traffic flow conditions and other model parameters under different lane-changing distances. The results showed that, as the traffic flow and the proportion of off-ramp vehicles increase, the required lane change spacing for vehicles increase accordingly. From the perspective of safety, factors such as large traffic volume [13] and high traffic density [14] could make lane-changing behavior risky, inhibiting the frequency of lane changes or increasing the duration. Contrary to AVs, the age of an HDV and the driver could affect the frequency of the lane-changing phenomenon and the safety of the vehicle [15]. A large speed difference between the lane-changing vehicle and the target lane also increased the hazard [16]. [PERSON] et al. [17] found that acceleration has an effect on lane changing with the analysis of the extracted trajectory features. The classification method for lane-changing maneuvers [18] confirmed the important influence of duration on lane-changing. [PERSON] et al. [19] studied the influence of lane markings on lane-changing behavior and divided its process into three stages, which provided a reference for the division of the lane-changing process in this paper.