## 5 Experiments and Discussion
### Verification and Results Discussion  
Classical SMC, TSMC, and the method proposed in this study were used for comparison. Two scenario tests were conducted using a MATLAB/Simulink-CarSim co-simulation experimental platform for self-driving vehicles. These tests were carried out on two-way four lane roads with a width of 3.75 m, and the ego vehicle speed is 10 m/s. This paper takes the low-speed lane-changing scenario as an example, and the results of the two lane change scenarios are shown in Figure 7 and Figure 8, respectively. In this paper, the results of different control methods are plotted in the same picture. Figure 7 shows four diagrams with important state information about the vehicle. As can be seen from the figure, the start time of the lane changing process is the time point of 8 s, and the lane-changing duration is 4 s. As can be seen from Figure 7a, among the three methods, the lane change effect of the controller designed by the SMC method is the most unstable. The SMC has a maximum wheel angle of more than 100 degrees, which is a very drastic lane change operation and even dangerous on the actual road. TSMC performed much better than SMC. Using TSMC method, it can be seen that the maximum peak angle of the commutator wheel is more than 40 degrees. The best performance is NNTSMC, which not only has the best performance in the peak angle of the steering wheel, but also has a stable performance in the whole lane change process. In the whole lane change process, the NNTSMC's curve is the smoothest from the result. In comparison, SMC and TSMC methods are much less stable. most stable manifestation. Therefore, NNTSMC still has the best performance among the three methods in terms of lateral acceleration. Figure 7: The experimental results of Scenario A.  
Figure 8: The experimental results of Scenario B. A similar conclusion can be drawn from Figure 7c. NNTSMC has the best performance in the angle index of yaw, and the local magnification effect of the original image can be seen from the enlarged small block diagram. We can find that the controller designed by the proposed method has the quickest transition process, the fastest control, and the highest precision. Yaw can get back to zero at the end of the lane change very accurately. In this paper, lateral distance is also a very important measurement index. The accuracy of lateral displacement reflects the accuracy of the vehicle controller. The smaller the error between the measured distance value and the desired value, the better the control effect of the method. From Figure 7d, we can see the lateral distance results of multiple methods. Although all methods finally stabilized at the value of about 1.875 m after lane change, only NNTSMC showed the best performance in the transition process as shown in the enlarged figure. High control precision and rapid control process are the abilities that an excellent control algorithm needs at the same time. Scenario B is an upgraded version of Scenario A. The common point of the two experiments is that the start time of the lane changing process is 8 s. The difference is that the lane change in scenario B lasts eight seconds. Therefore, the first four seconds of Figure 8a-d is the same process as Figure 7. The difference is, in the second lane change process, the four seconds it takes to switch back into the original lane in Scenario B. From these four subgraphs in Figure 8, it can be seen that the overall performance of NNTSMC maintains outstanding results in the continuous lane change scenario. In terms of wheel angle stability, safety, and comfort reflected by lateral acceleration, and further lateral distance accuracy, the proposed method has the best performance. In comparison, both SMC and TSMC failed to show better accuracy and stability.