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The development of an adaptive Lane Keeping Assistance System (LKAS) is presented, focusing on enhancing vehicular lateral stability and alleviating driver workload.Traditional LKAS with static parameters struggle to accommodate varying driver behaviors.Addressing this challenge, the proposed system integrates adaptive driver characteristics, aligning with individual driving habits and intentions.A novel lane departure decision model is introduced, employing time-space domain fusion to effectively discern driver's lane change intentions, thus informing system decisions.Further innovation is realized through the application of reinforcement learning theory, culminating in the creation of a master controller for lane departure intervention.This controller dynamically adjusts to driver behavior, optimizing lane keeping accuracy.Extensive simulations, coupled with hardware-in-the-loop experiments using a driving simulator, substantiate the system's efficacy, demonstrating marked improvements in lane keeping precision.These advancements position the system as a significant contribution to the field of driver assistance technologies.
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Wei et al. (2024) studied this question.
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