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 testbed 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.
An adaptive cruise control system based on self-learning algorithm for driver characteristics
Ein adaptives Tempomatsystem auf Basis des Selbstlernalgorithmus für die Fahrercharakteristik
2009
10 Seiten, 7 Bilder, 1 Tabelle, 10 Quellen
Conference paper
English
Research on adaptive cruise control based on driver characteristics
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