An Adaptive Cruise Control System based on Self-Learning Algorithm for Driver Characteristics

摘要

An Adaptive Cruise Control system prototype based on self-learning algorithm for driver characteristics is presented. To imitate the driver operations during car-following, a driver model is developed to generate the desired throttle depression and braking pressure. A self-learning algorithm for driver characteristics is proposed based on the Recursive Least Square method with forgetting factor. Using this algorithm, the parameters of the driver model are real-time identified from the data sequences collected during the driver manual operation state, and the identification result is applied during the system automatic control state. The system is verified in a driving assistance system test bed with electronic throttle and electro-hydraulic brake actuators. The experimental results show that the self-learning algorithm is effective and the system performance is adaptive to driver characteristics.