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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.

The ability to recognize traffic vehicles’ lane change maneuver lays the foundation for predicting their long-term trajectories in real-time, which is a key component for Advanced Driver Assistance Systems (ADAS) and autonomous automobiles. Learning-based approach is powerful and efficient, such approach has been used to solve maneuver recognition problems of the ego vehicles on conventional researches. However, since the parameters and driving states of the traffic vehicles are hardly observed by exteroceptive sensors, the performance of traditional methods cannot be guaranteed. In this paper, a novel approach using multi-class probability estimates and Bayesian inference model is proposed for traffic vehicle lane change maneuver recognition. The multi-class recognition problem is first decomposed into three binary problems under error correcting output codes (ECOC) framework.