User-Driven Adaptation: Tailoring Autonomous Driving Systems with Dynamic Preferences

TL;DR

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.

Abstract

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.

Figures (3)

• Figure 1: The Motivating Scenario and User Study built on Unreal Engine.
• Figure 2: User Study Results
• Figure 3: An Illustration of Crossover and Mutation Operations in GA-based Preference Update.