Self-Learning Optimal Cruise Control Based on Individual Car-Following Style

This study aims to develop an optimal cruise controller that can automatically adapt to individual car-following style. First, the adaptive cruise control (ACC) problem is formulated as a linear quadratic optimal control, and an optimal control law containing the longitudinal acceleration of the target vehicle is derived. Then, a certain number of individual car-following styles are predefined on the basis of the proposed optimal cruise controller. Thereafter, a car-following style learning algorithm is proposed to quantify the closeness of the predefined individual car-following style to the specific driver, and a proper style is thus determined for the specific driver by using this learning algorithm. On the basis of the learned car-following style, the proposed optimal cruise controller can adapt itself to individual car-following style. Finally, the proposed self-learning optimal cruise controller is evaluated through simulation and experimental tests. Results show that the control behavior of the proposed self-learning optimal controller is closer to that of the human driver than that of a factory-installed ACC.

Automated summary

This study developed an optimal cruise controller that automatically adapts to individual car-following styles. By using a learning algorithm to quantify closeness to predefined styles, the controller was able to determine and adapt to a proper style for specific drivers. Simulation and experimental tests showed that the controller's behavior was closer to that of human drivers than factory-installed ACC systems.