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Advanced Driver Assistance Systems (ADAS) are increasingly important inimproving driving safety and comfort, with Adaptive Cruise Control (ACC) beingone of the most widely used. However, pre-defined ACC settings may not alwaysalign with driver's preferences and habits, leading to discomfort and potentialsafety issues. Personalized ACC (P-ACC) has been proposed to address thisproblem, but most existing research uses historical driving data to imitatebehaviors that conform to driver preferences, neglecting real-time driverfeedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACCframework that incorporates driver feedback adaptation in real time. Theframework is divided into offline and online parts. The offline componentrecords the driver's naturalistic car-following trajectory and uses inversereinforcement learning (IRL) to train the model on the cloud. In the onlinecomponent, driver feedback is used to update the driving gap preference in realtime. The model is then retrained on the cloud with driver's takeovertrajectories, achieving incremental learning to better match driver'spreference. Human-in-the-loop (HuiL) simulation experiments demonstrate thatour proposed method significantly reduces driver intervention in automaticcontrol systems by up to 62.8%. By incorporating real-time driver feedback, ourapproach enhances the comfort and safety of P-ACC, providing a personalized andadaptable driving experience.

Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control