Iterative Learning-Based Arbitration in Shared Vehicle Control: Methodological Formulation and Driver-in-the-Loop Results

Shared control, which merges the dynamic inputs of human drivers with vehicle automation, has attracted considerable attention due to its potential to enhance both safety and driver satisfaction. However, most existing shared control strategies are based on one-size-fits-all designs, neglecting the fact that the optimal level of sharing will typically depend on the individual driver and road characteristics. In light of these limitations, along with the observation that routes are commonly repeated, we propose an iterative learning control algorithm that divides a closed-loop driving circuit into discrete segments, enabling online estimation of the driver's performance over each segment. By adapting arbitration weights based on these driver-specific and segment-specific estimates, our method seeks to reduce lateral tracking errors, reduce path completion time, and increase secondary task performance. To validate this approach, we conducted driver-in-the-loop simulator tests with 20 participants, each driving repeatedly on the closed-loop circuit. The results demonstrate that our personalized strategy significantly improves driving performance.