Improvement of Adaptive Cruise Control System Performance on Sloped Roads Based on Adaptive Neuro-Fuzzy Inference System

Adaptive cruise control is a system that maintains an appropriate distance from vehicles ahead while driving. Although Adaptive cruise control systems operate stably on flat roads, they can become unstable on sloped roads. This study presents a method to enhance the performance of Adaptive cruise control systems on sloped roads using fuzzy logic control techniques and the adaptive neuro-fuzzy inference system. First, an Adaptive cruise control system is described based on state-space equations. Then, a fuzzy logic controller is designed and applied to the Adaptive cruise control system, and improved Adaptive cruise control performance is verified via computer simulations. The two inputs of the fuzzy logic controller are the speed error and the distance between vehicles. The output is the improved control force. In addition, to achieve enhanced performance under varying road conditions, the adaptive neuro-fuzzy inference system is applied. The adaptive neuro-fuzzy inference system is designed by training the designed fuzzy logic controller with data extracted from scenarios in which the road gradient angle changes. Simulations are performed by applying the adaptive neuro-fuzzy inference system applied to the Adaptive cruise control system. These simulations demonstrate that improved results are obtained even under new road conditions. This paper demonstrates that the design of the adaptive neuro-fuzzy inference system controller can effectively enhance the performance of the Adaptive cruise control system on sloped roads.