发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种偏好自适应滚动时域控制框架，通过自适应权重和贝叶斯推断优化自动驾驶中的乘客舒适度与晕动症，实验验证了其有效性和安全性。
AI 中文摘要
本文提出了一种用于自动驾驶的偏好自适应滚动时域控制框架，该框架考虑了乘客偏好和晕动症易感性。我们构建了一个有限时域最优控制问题，其中速度、加速度舒适度和晕动症的权重自适应调整，而碰撞风险的权重保持固定。我们使用运动病症状分类（MISC）量表上的个体化模型在线预测晕动症，并通过贝叶斯推断从基于情绪的两两比较中离线更新偏好权重。一个确定性的安全监督器检查规划轨迹，并在必要时修改控制指令。我们使用三种模拟乘客画像在三种晕动症易感水平下评估该框架。学习到的权重产生了不同的闭环行为，在九个场景中有七个场景的平均评估情绪得分有所提高。在完整方法的实验中未发生碰撞，而安全监督器在1.621%的学习帧中进行了干预。
英文摘要
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.
