Privacy-Preserving Personalized Autonomous Vehicle Lane Change Using Inverse Reinforcement Learning

This paper presents an approach to model driver lane change behavior using maximum entropy inverse reinforcement learning (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaxEnt</i> IRL). The proposed method aims to generate personalized lane change trajectories that reflect individual driving preferences while ensuring user privacy. To achieve this, driving data are collected from individual driver and used to train the model, while scale-based transformation is utilized to encrypt the data during cloud-based training. Bezier curves are employed to parameterize the lane change trajectories due to their ability to produce smooth, continuous paths. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaxEnt</i> IRL algorithm is then used to infer a reward function that represents each driver's preferences by learning optimal weights for a cost function that describes the lane change behavior. The proposed approach is tested over several real-world data to demonstrate its effectiveness in capturing personal driving styles under various conditions. The proposed trajectory encryption approach is compared to a benchmark differential privacy mechanism, and the results clearly show that the proposed method outperforms. Experimental results demonstrate that the proposed method can generate lane change paths that closely align with the behavior exhibited by individual driver, offering an approach for personalization in autonomous driving.