The rapid development of urban transportation has increased the number of motor vehicles, causing serious traffic congestion and frequent accidents, which challenge the efficiency of transportation systems. To improve autonomous driving performance and relieve traffic pressure, this study proposes a vehicle-assisted driving system combining a fuzzy neural network with a proportional integral derivative controller. The longitudinal control structure is analysed, and controllers at different levels are designed. Experimental results indicate that the maximum overshoot is reduced by 11.4% compared with the traditional proportional integral derivative controller. The rise time and adjustment time decrease by 0.3 s and 0.9 s, and the response stabilises at about 0.5 s with almost no overshoot. In practical road tests, the controller reaches the desired speed within 10 s and maintains a stable following distance, demonstrating strong robustness and effective control performance.
Yunpeng Li (Thu,) studied this question.