## 1 Introduction  
Lane change is one of the most common maneuvers for vehicles on the road, and it needs drivers to consider risk avoidance in multiple directions at the same time, which is usually a driving behavior with high-risk coefficients. In 1999, there were more than half a million lane change-related accidents in the United States in one year [1]. Data show that, during lane changes, driver stress increases significantly, and driving is more prone to errors, leading to higher risks. Vehicle lane-changing behavior is influenced not only by the surrounding traffic environment but also by the surrounding traffic flow, hence research into autonomous lane-changing technology is crucial [2]. To accomplish autonomous lane-changing driving, vehicles can employ the vehicular networks [3] or use the on-board sensors to gather information about the surrounding traffic [4]. Furthermore, the built-in sensors can help vehicles with measuring operating conditions [5]. These devices may allow them to make more informed decisions about the lane-changing procedure, improving vehicle safety, comfort, and efficiency [6]. It is worth mentioning that related technologies related to vehicular networks and vehicle control system, such as authentication and privacy schemes, are also important guarantees for the reliable operation of vehicles [7, 8, 9]. The key technologies of autonomous lane change can be divided into three categories [10]: lane change decision, lane change path planning, and lane change path tracking control. A lane change decision provides a logical direction for vehicle lane shift. Currently, the most frequent lane shifting models utilized in vehicle lane-changing decision-making mechanisms are the Gipps model [11], the MITSIM model [12], the AIMSUN2 model [13], the CA model [14], the CORSIM model [15], and the SITRAS model [16]. The design of the lane change path must be taken into consideration if it is decided to perform a lane change after the judgment of lane change decision [17]. The purpose of the lane change path design is to make the vehicle have a virtual path as a reference in the process of lane change, so that the lane change action can be completed more safely and smoothly [18; 19]. The planning of lane change trajectory needs to transform the time and distance planning problems into constraint optimization problems, and the evaluation indexes in the driving process need to be considered to calculate the reference trajectory satisfying the safety, comfort, and lane change efficiency [20]. The polynomial method [21], trapezoidal acceleration method [22], arc trajectory planning method [23], sine function method [24], and other trajectory functions are indeed common lane change reference trajectory design methods. Designers can choose a solution according to the characteristics of application scenarios as well as the advantages and disadvantages of each method [25]. It is necessary to investigate autonomous vehicle lane-changing to improve driving safety and road smoothness. The actual situation of a vehicle lane-changing procedure can be simplified to a straightforward lane change scenario, which includes the target vehicle \(A0\), vehicle \(A1\) in front of the lane, and vehicle \(A2\) in the neighboring lane, using an urban two-lane road as an example. Figure 1 depicts the schematic diagram of a lane-changing scenario. Vehicle \(A0\) has lane change intention because it is dissatisfied with the current lane's speed due to the vehicle \(A1\)'s slower speed in front of it. After vehicle \(A0\) has the demand for a lane change, it should not have a serious impact on the traffic flow on the basis of ensuring its driving demand. Vehicle \(A0\) moves into the stage of preparing for a lane change in phases \(t_{0}-t_{1}\). Vehicle \(A0\) crosses the two-lane decomposition line at time \(t_{2}\) in phases \(t_{1}-t_{3}\), which is a lane-changing operation. Phases \(t_{1}-t_{3}\) correspond to the lane-changing process, where vehicle \(A0\) crosses the two-lane separation line at \(t_{2}\).