# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 1 Introduction  
In recent years, with the continuous increase in vehicle ownership, the problem of traffic safety has been deteriorating. Smart vehicles and unmanned driving that can effectively improve traffic safety are being vigorously developed and applied. Path planning that generates a driving route from the initial point to the destination is one of the keys features of unmanned driving technology. For autonomous vehicles, the constraints of vehicle kinematic and dynamics are important to be studied in path planning algorithm in addition to the obstacles avoidance. Path planning methods for autonomous vehicles have been widely studied. There are some advanced path planning methods for autonomous road vehicles, such as artificial potential field methods [1; 2] and optimal control [3; 4]. The obstacles, road structures, and vehicle dynamics are considered in these proposed methods. However, there is a sudden change in the curvature of the trajectory generated by these methods, and it is often necessary to smooth the generated path and increase the workload. Many studies about curvature-continuous path planning methods have been conducted [5; 6; 7]. In these studies, some continuous curves such as Bezier curve are used fortrajectory planning. [PERSON] et al [7] proposed a practical path planning algorithm based on Bezier curves for autonomous vehicles operating under waypoints and corridor constraints. The path planning algorithm combines a set of low-degree of Bezier curve segments smoothly to generate the reference trajectory. However, since the planned path has difficulty meeting all comfort requirements of different passengers, it may still increase the probability of the motion sickness. [PERSON] and [PERSON] [8] found that the proportion of people with motion sickness riding smart vehicles is much higher than traditional vehicles. Other studies have proven this conclusion [9; 10]. As a result, conventional path panning algorithm is not sufficient to meet motion comfort requirements in these scenarios. However, under any condition, an experienced driver is always able to find an optimal path to keep the vehicle moving steadily. Thus, it is necessary to study the driving paths of experienced drivers. Lane-changing is an important part of autonomous driving behavior in arterial road traffic [11], which involves changes in both longitudinal and lateral velocity as well as movement in the presence of other moving vehicles [12]. Therefore, many studies have been carried out on lane change of autonomous vehicle [13; 14]. Dubins path, the shortest path for a wheel-drive robot consisting of a set of two circular arcs and line segments [15], is one of the most well-known and widely studied methods to generate a smooth path [16]. [PERSON] et al [17] presented a path planning method using circle curve as the lane-changing path for autonomous vehicle. However, the path has a fatal drawback that the curvature at the joint nodes connecting the lines and arcs is discontinuous.Ren et al [18] presented a lane-changing trajectory generating method based on the vehicle lateral acceleration during lane changing meeting the constraints of positive and negative trapezoid. [PERSON] et al. [19] used the seven-order polynomial as the path expression of the lane-changing path. The location of lane changing ends is determined according to the average lane changing time assuming that the longitudinal speed is constant. However, the characteristics of different drivers in the actual lane changing operation were not considered in these studies. The artificial neural network is a powerful, nonlinear, and adaptive mathematical model [20]. It has been used extensively and successfully in various fields, including image processing [21], pattern recognition [22] and voice recognition [23]. It is difficult to describe the characteristics of the actual driver's lane-changing operation by accurate mathematical modeling. Thus, the neural network is utilized to establish the lane-changing path model.