Research on the Impact of Different Controllers on the Adapative Cruise Control System

Adaptive Cruise Control (ACC) system is a well-known Advanced Driver Assistant Systems (ADAS) that offers safe, comfortable, and fuel-efficient self-driving capabilities. It utilizes a combination of hardware and software to follow other vehicles, effectively. While the design and control of this system are complex, its user interface ensures ease of use and understanding. This paper aims to demonstrate the application of different control algorithms with the ACC system. Firstly, it introduces the Model Predictive Controller (MPC), Levenberg-Marquardt Neural Network (LM NN), Bayesian Regularization Neural Network (BR NN), and Scaled Conjugate Gradient Neural Network (SCG NN). Subsequently, the paper presents a practical implementation of these three neural networks in conjunction with the Classical ACC controller. The performance of MPC with these three emulated neural networks is then evaluated. Finally, the paper concludes by establishing an evaluation and summary of the performance of these controllers.