As a core application of V2X technology, vehicle–road collaboration enables dynamic coordination among road users (pedestrians, vehicles), infrastructure, and networks through real-time, omnidirectional information exchange. This system represents a pivotal solution for addressing critical transportation challenges, including traffic congestion, safety risks, and environmental sustainability. Its experimental teaching, as the core linkage of theoretical innovation and technical verification, is of vital importance to the cultivation of intelligent transportation talents. Compared with traditional experimental teaching, the V2X semi-physical simulation platform effectively reduces capital investment, completely eliminates the safety risks of actual road tests, and emulates the real traffic environment. To verify the teaching effectiveness of this platform, based on the OBE concept and the BOPPPS teaching method, this study constructed an experimental curriculum framework driven by learning goals and conducted an empirical analysis taking global path planning as an example. Teaching evaluation adopts a combination of subjective and objective methods: Subjective evaluation is conducted through questionnaire surveys, and the proportion of those satisfied with the teaching effect reached more than 80%. The objective evaluation consists of eight performance indicators before class, during class and after class. Through reliability analysis, the performance of students in the observation group was shown to increase by 17.39% compared with that in the control group. The results show that the experimental teaching mode based on the V2X semi-physical simulation platform significantly improves the teaching effectiveness of the vehicle–road collaboration course compared with traditional methods.

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments.