In the realm of autonomous vehicles, dynamic user preferences are critical yet challenging to accommodate. Existing methods often misrepresent these preferences, either by overlooking their dynamism or overburdening users as humans often find it challenging to express their objectives mathematically. The previously introduced framework, which interprets dynamic preferences as inherent uncertainty and includes a ``human-on-the-loop'' mechanism enabling users to give feedback when dissatisfied with system behaviors, addresses this gap. In this study, we further enhance the approach with a user study of 20 participants, focusing on aligning system behavior with user expectations through feedback-driven adaptation. The findings affirm the approach's ability to effectively merge algorithm-driven adjustments with user complaints, leading to improved participants' subjective satisfaction in autonomous systems.

翻译：在自动驾驶车辆领域，动态用户偏好至关重要但难以兼顾。现有方法常错误表征这些偏好，要么忽略其动态性，要么过度加重用户负担——因为人类往往难以用数学方式表达自身目标。先前提出的框架将动态偏好解读为固有不确定性，并引入"人在回路"机制，使用户能在对系统行为不满时提供反馈，从而弥补了这一缺陷。本研究通过一项20人用户实验对该方法进行进一步强化，聚焦于通过反馈驱动自适应实现系统行为与用户期望的对齐。研究结果证实了该方法能有效融合算法驱动调整与用户投诉，进而提升参与者对自主系统的主观满意度。