## 6 Conclusions  
This paper proposes a novel lane-changing control strategy based on the NNTSMC method aiming at improving lane-changing reliability and driving comfort. Lane-changing control strategy integrates a non-singular fast terminal sliding mode control method and neural networks, which possess certain anti-disturbance ability.  
As the key technology of vehicle intelligent driving, lane-changing control has great research significance. Based on the established vehicle dynamics model and lane-changing controller, the results are verified in MATLAB/Simulink-CarSim co-simulation environments. CarSim provides a vehicle dynamics model and vehicle related parameter information. The lane-changing controller is constructed in Simulink.  
In order to verify the function of the lane-changing controller based on the proposed control strategy, several experiments are also designed in this paper. Simulation experiments with two scenario tests performed on the co-simulation platform confirmed the effectiveness and superiority of the proposed control scheme over the traditional SMC approach and TSMC. The lane-changing trajectory planning model and lane-changing control model established above are simulated and tested. Through the analysis of simulation data, the superiority and effectiveness of the control strategy are verified. The proposed method, in comparison to SMC, has a performance improvement of at least 58.0%, and compared to TSMC, it has a performance improvement of at least 34.1%. Simulation results show that the proposed method can make the vehicle track the lane center with a small error and maintain good driving quality.  
Although the test results are ideal, compared with the actual lane-changing scene, there are inevitably some problems that have not been considered comprehensively. In the future, we will attempt to conduct the verification experiment using a physical vehicle. In practical applications, when modeling the vehicle dynamics, the uncertainty of its parameters and the uncertainty of the external environment, such as the friction of mechanical components, wheel skids, etc., will affect the accuracy of trajectory tracking. Therefore, when designing a higher standard lane change controller, it is essential to establish a dynamic model with richer information and base the control algorithm on it.  
\begin{table}
\begin{tabular}{l l l c} \hline \hline  & & **Method Name** & \(\mathbf{V_{x}}\) **= 10 (m/s)** \\ \hline \multirow{4}{*}{Scenario A} & \multirow{2}{*}{Maximum lateral acceleration (m/s\({}^{2}\))} & SMC & 3.065 \\  & & TSMC & 1.925 \\  & & Proposed & **1.269** \\ \cline{2-4}  & \multirow{2}{*}{Maximum lateral error (m)} & SMC & 0.281 \\  & & TSMC & 0.252 \\  & & Proposed & **0.118** \\ \hline \multirow{4}{*}{Scenario B} & \multirow{2}{*}{Maximum lateral acceleration (m/s\({}^{2}\))} & SMC & 3.065 \\  & & TSMC & 1.965 \\ \cline{1-1}  & & Proposed & **1.272** \\ \cline{1-1} \cline{2-4}  & \multirow{2}{*}{Maximum lateral error (m)} & SMC & 0.377 \\ \cline{1-1}  & & TSMC & 0.331 \\ \cline{1-1}  & & Proposed & **0.137** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the co-simulation.  
**Author Contributions:** Conceptualization, C.W.; modeling, simulation, and validation, C.W.; writing, original draft preparation, C.W.; supervision and critical review, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Education Commission, the Major Natural Science Project of Scientific Research and Innovation Program (No. 2021-01-07-00-07-E00092), the Ministry of Science and Technology of the People's Republic of China, and the National Key Research and Development Program Grant/No. 2021 YFB1600100.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.