Towards Adapting Cars to their Drivers

Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. While generic packages such as Weka were successful in learning drivers' behavior, we found that improved learning models could be developed by adding information on drivers' demographics and a previously developed model about different driver types.