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arXiv · 2610.06539

Preference-Adaptive Control in Autonomous Driving

Zhongyu Mo·Shanting Wang·Huaijin Hu·Andreas A. Malikopoulos
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Abstract

In this paper, we present a preference-adaptive receding-horizon control framework for autonomous driving that accounts for passenger preferences and motion-sickness susceptibility. We formulate a finite-horizon optimal control problem with adaptive weights for speed, acceleration comfort, and motion sickness, while maintaining a fixed weight for collision risk. We predict motion sickness online using an individualized model on the Motion Illness Symptoms Classifi- cation (MISC) scale and update the preference weights offline from emotion-derived pairwise comparisons using Bayesian inference. A deterministic safety supervisor checks the planned trajectory and modifies the control command when necessary. We evaluate the framework using three simulated passenger profiles under three motion-sickness susceptibility levels. The learned weights yield distinct closed-loop behaviors, and the mean evaluation emotion score improves in seven of nine scenarios. No collisions occur in the full-method experiments, while the safety supervisor intervenes in 1.621% of the learning frames.

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Zhongyu Mo, Shanting Wang, Huaijin Hu, Andreas A. Malikopoulos. 2026-10-05. Preference-Adaptive Control in Autonomous Driving. https://arxiv.org/abs/2610.06539

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