Personalized autonomous driving: An integrated framework with sparse incremental Gaussian process regression and optimal control

Personalized autonomous driving systems are critical to improving user acceptance and trust. However, existing personalization methods mainly rely on imitating manual driving behaviors, which often fail to consider the significant shift in user preferences when transitioning from an active “driver” role to a passive “supervisor” role. Furthermore, traditional learning-based methods face challenges in computational scalability, impeding efficient online adaptation to real-time user feedback. To address these issues, this paper proposes a user-feedback-driven personalized autonomous driving framework that integrates online learning and optimal control. First, a Sparse Incremental Gaussian Process Regression (SI-GPR) framework with a tailored sample management mechanism is introduced. Distinct from conventional modular integration, our framework is uniquely architected to capture the non-stationary transition of user preferences during the role shift from active “driver” to passive “supervisor.” SI-GPR effectively models evolving psychological expectations by dynamically updating a sparse inducing set that prioritizes recent human feedback. Crucially, this mechanism maintains constant computational complexity. Second, the learned personalized parameters are embedded into a Constrained Optimal Control Problem (OCP) for trajectory planning, which dynamically balances safety, comfort, and tracking efficiency. Finally, the proposed framework is validated using a Human-in-the-Loop (HIL) simulation platform. Experimental results supported by subjective ratings and physiological indicators such as heart rate variability demonstrate that the proposed method significantly reduces user interventions and mental workload compared with baseline methods. This work provides a closed-loop solution for aligning autonomous driving behavior with the evolving user expectations in the supervisor role.