A Method of Identifying Personalized Car-Following Characteristics for Adaptive Cruise Control System

Although it's very common nowadays to experience onboard adaptive cruise control systems (ACCS), most of them are initialized with fixed parameters and little consideration is taken to adapting to different drivers. In order to improve the ride comfort and operation familiarity of drivers when using the ACCS, driver's personalized car-following (CF) characteristics should be considered in ACCS developments. In this paper, an ACCS framework with CF characteristics identification and application is designed, where the desired safety margin (DSM) model is utilized to describe driver's CF behaviors. In view of the limited performance of on-board computers, a simple method for CF characteristics identification from the sight of driver's physiology and psychology mechanism is proposed, which tries to estimate DSM model parameters on the basis of CF data. With the proposed method, the CF data under manual driving are collected and filtered for the identification of drivers' response time, upper and lower limits of DSM and sensitivity factors for acceleration and deceleration iteratively. And the latest iteration results of DSM model parameters would then be applied into the ACCS when it's enabled. Several naturalistic driving tests are adopted for the verification whose results support that, the proposed method could perform well in identifying drivers' CF characteristics online by collecting and analyzing manual driving data with a much smaller resource consumption when compared with genetic algorithm (GA). Besides, with the increase of CF data volume, the identified results are also becoming more and more stable and robust. Therefore, the proposed ACCS framework is able to imitate different drivers.