In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics.
The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems.
Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle. Some high precision sensors were mounted in both test vehicles, such as RT 3002®, RT range®, radar and pedal pressure sensor. The traffic vehicle was set in different driving modes, include cruise mode, decelerating slowly, accelerating slowly, hard braking, hard acceleration, cutting out the lane, cutting into the line and sinusoidal movement. The host vehicle was operated behind the traffic vehicle by 84 drivers selected randomly. The drivers’ driving styles were evaluated via both subjective and objective indicators. The subjective indicators were based on the questionnaires by interviewing the drivers and the passengers. The objective indicators were based on the parameters extracted from the specific test conditions. The vehicle-following data were collected and analyzed via MATLAB® which was further trained by Gaussian Mixture Model. With regarding to the multi-mode switching strategy, the parameters of different driver’s characteristics were considered in the fuzzy PID control method to improve the adaptivity and accuracy in the switching process. Meanwhile, different fuzzy rules and fuzzy membership functions were applied for every type of drivers to calculate the best parameters for the personalized ACC control strategy. The control modes include cruise, steady following, approaching and hard braking. The different driving characteristics were also added into every control strategy via road experimental data and analyzed results from Gaussian Mixture Model. Compared with the existing ACC, the system proposed in this paper has great advantages on not only the adaptivity and human acceptance, but also the performance of the system itself which was verified on the simulation platform PanoSim®.