Modeling and Characterization of Driving Styles for Adaptive Cruise Control in Personalized Autonomous Vehicles

For autonomous vehicles to gain widespread customer acceptance, safety and reliability are not nearly enough. Comfort and familiarity of the ride is also of essential importance. Because these are highly subjective factors, autonomous vehicles must be able to adopt personal driving styles to meet individual preference. The adaptive cruise control (ACC) system is a critical function performed by the autonomous vehicle and much research effort has been devoted to the development of a system that acts as a human driver. However, studies which investigate ACC models capable of learning a driving style are limited. In this paper, we propose a method to extract quantifiable parameters which represent a drivers’ driving style and apply these parameters to personalize the longitudinal control of an autonomous vehicle. We then develop a longitudinal driver model that integrates those parameters to enable the ACC system to mimic the driving style of the driver. Finally, the effectiveness of the extraction method and the driver model are obtained through simulation.