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

2025-01-12

AI Summary

Designing visualization feedback for autonomous vehicles (AVs) remains challenging due to the difficulty of simultaneously optimizing user comprehension, trust, perceived safety, and cognitive load. Method: This paper proposes a human–machine collaborative optimization framework that dynamically generates personalized visualizations for detection, prediction, and planning—tailored to individual user needs. It introduces a Warm-Start multi-objective Bayesian optimization (MOBO)-driven “human-in-the-loop” (HITL) paradigm, overcoming limitations of static, one-size-fits-all interfaces. The approach employs dual-path design space modeling (integrating expert knowledge and user input), online experiments (N=117), and multidimensional evaluation. Contribution/Results: Results demonstrate statistically significant improvements in user trust, acceptance, perceived safety, and predictability (p<0.01), without increasing cognitive load. The framework enables efficient exploration of high-dimensional visualization design spaces, yielding personalized, interpretable, and scalable in-vehicle visualization solutions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Autonomous Vehicles
• Visual Feedback Optimization
• User Trust and Acceptance

Innovation

• Multi-objective Bayesian Optimization
• Autonomous Vehicles Visualization
• User Trust and Safety Perceptions