Title :
An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics
Author :
Jianqiang Wang ; Lei Zhang ; Dezhao Zhang ; Keqiang Li
Author_Institution :
Tsinghua Univ., Beijing, China
Abstract :
A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver´s operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver´s pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.
Keywords :
adaptive control; adaptive filters; braking; collision avoidance; control engineering computing; data analysis; driver information systems; electrohydraulic control equipment; hydraulic actuators; learning systems; mechanical engineering computing; pressure control; road traffic control; self-adjusting systems; vehicle dynamics; adaptive cruise control; adaptive longitudinal driving assistance system; automatic braking activation; braking pressure; data analysis; driver behavior; driver car-following tests; driver characteristics; driver model; electrohydraulic brake actuator; electronic throttle depression; forgetting factor; forward collision avoidance; forward collision warning; gap closing; manual operation phase; pedal deflection timing; real traffic environments; recursive least-square method; self-learning algorithm; system validation; Adaptation models; Adaptive systems; Algorithm design and analysis; Analytical models; Data models; Vehicle dynamics; Vehicles; Car following; driver characteristics; driving-assistance systems; self-learning algorithm;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2012.2205143