OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience

TL;DR

OptiCarVis employs Bayesian optimization with human feedback to personalize AV visualization designs, significantly improving user trust, safety perception, and acceptance without added cognitive load.

Contribution

This paper introduces OptiCarVis, a novel HITL MOBO approach for optimizing AV visualizations tailored to user preferences, surpassing traditional static designs.

Findings

Significant improvements in trust and safety perceptions.

Enhanced user acceptance of AV visualizations.

Effective exploration of diverse visualization design options.

Abstract

Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.