Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. Advanced driver-assistance systems (ADASs) aim to assist drivers during lane change maneuvers. A system that is developed for an average driver or all drivers will have to be conservative for safety reasons to cover all driver/vehicle types. Such a conservative system may not be acceptable to aggressive drivers and could be perceived as too aggressive by the more passive drivers. An ADAS that takes into account the dynamics and characteristics of each individual vehicle/driver system during lane change maneuvers will be more effective and more acceptable to drivers without sacrificing safety. In this paper, we develop a methodology that learns the characteristics of an individual driver/vehicle response before and during lane changes and under different driving environments. These characteristics are captured by a set of models whose parameters are adjusted online to fit the individual vehicle/driver response during lane changes. We develop a two-layer model to describe the maneuver kinematics. The lower layer describes lane change as a kinematic model. The higher layer model establishes the kinematic model parameter values for the particular driver and represents their dependence on the configuration of the surrounding vehicles. The proposed modeling framework can be used as a kernel component of ADAS to provide more personalized recommendations to the driver, increasing the potential for more widespread acceptance and use of ADAS. We evaluated the proposed methodology using an actual vehicle and three different drivers. We demonstrated that the method is effective in modeling individual driver/vehicle responses during lane change by showing consistency of matching between the model outputs and raw data.