Multi-Session User Experience Assessments of Computationally Optimized Automated Vehicle Functionality Visualizations

2026-07-30

AI Summary

This study addresses the high cost and low efficiency of traditional approaches to evaluating autonomous driving visualization designs, which hinder systematic optimization of passengers’ trust, sense of safety, and cognitive load. To overcome these limitations, this work proposes a novel framework that introduces multi-objective Bayesian optimization into multi-session user studies, enabling efficient and scalable subjective-experience-driven design optimization. Through an online experiment with 74 participants and rigorous quantitative analysis, the study successfully identifies specific combinations of visualization parameters that significantly enhance users’ trust, perceived safety, and predictability. These findings demonstrate the effectiveness and practicality of the proposed method for optimizing complex human–machine interaction designs in real-world autonomous systems.

Abstract

Understanding automated vehicles (AVs) is crucial to improving their acceptance. Numerous approaches to visualizing relevant traffic information to passengers have been proposed and empirically evaluated. As this is time-consuming, costly, and reduces the possible design parameters, we employed multi-objective Bayesian optimization to optimize the design of visualizations in AVs. In particular, we evaluated multi-session aspects involving iterative optimization. We optimized the design for passenger trust and perceived safety while minimizing cognitive load. Results from an online study (N=74) show that this method effectively identifies visualization design parameter values that improve trust, safety, and predictability while making the design process more efficient and scalable. However, shortcomings of the computational approach when optimizing for subjective measurements are highlighted and discussed.

Problem

• automated vehicles
• visualization design
• user trust
• perceived safety
• cognitive load

Innovation

• Bayesian optimization
• automated vehicle visualization
• multi-session evaluation
• user trust
• cognitive load