User-Driven Adaptation: Tailoring Autonomous Driving Systems with Dynamic Preferences
arxiv(2024)
摘要
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.
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