## 5 Experiments and Discussion
### Scenario Study  
This study verifies the performance of the proposed lane-changing strategy in the MATLAB/Simulink-CarSim co-simulation environments. CarSim provides vehicle dynamics model and vehicle-related parameter information. The algorithm of the lane change controller is built in Simulink. In the co-simulation, the steering wheel angle is input into the vehicle dynamics model, and the relative position information, lateral and longitudinal velocity, yaw angle and other related parameters of the vehicle are output variables, and these state variables are input into the lane change controller. The steering wheel angle can be output to the vehicle dynamics model for vehicle attitude control, and the lane change controller can obtain the new control law value based on the input vehicle-related parameter information. The mission of trajectory tracking control is ultimately accomplished by the vehicle dynamics model, which circulates the process and outputs the vehicle state information at the subsequent instant.  
In order to verify the effectiveness of the lane-changing control strategy proposed in this paper, two typical lane-changing test scenarios are constructed in the following experiment.  
Scenario A tested the scenario in which the ego vehicle changed lanes from the current lane and continued driving in another lane. This scenario primarily validates the proposed framework for dealing with emergency obstacles in front of the current lane or being forced to change lanes because the front vehicle is driving at a low speed. In this scenario, the lane change operation only needs to be performed once.  
Experimental scenario B, namely overtaking, tested the scenario in which a vehicle changed lanes from the current lane and then changed lanes again into the original lane to continue driving. This scenario primarily validates the performance of the proposed framework in a similar environment where overtaking is required. The process of overtaking is a combination of two lane-change processes. Finally, the vehicle completed the lane change process while remaining in the original lane.  
Figure 6 is an animated screenshot of scenario B. As can be seen from the animation screenshot, before lane change, the ego vehicle runs in the original lane with a good distance. Due to the slow speed of the vehicle in front in the original lane, the distance between vehicles gradually decreases. After the lane-changing demand is generated and the lane-changing decision is formed and executed after the lane-changing condition judgment, the target vehicle gradually moves from the original lane to the target lane along the planned lane-changing trajectory with a small driving error. At the end of the lane change, the target vehicle runs smoothly in the target lane. The screenshot consists of eight sub-images. Figure 6a shows the vehicle perceiving the vehicle ahead in the current lane. Figure 6b shows that the test vehicle has moved close to the front vehicle along the current lane because the speed of the front car is low, and it needs to be forced to change lanes. Figure 6c,d represent the first lane change process, and Figure 6e represents the completion of the first lane change. Figure 6f,g show the second lane-change process. Figure 6h shows the completion of the second lane change. Traffic continued in the same lane.