Improving External Communication of Automated Vehicles Using Bayesian Optimization

TL;DR

This paper uses multi-objective Bayesian optimization with 37 participants to improve external human-machine interfaces in automated vehicles, balancing communication effectiveness, trust, and safety perceptions.

Contribution

It introduces a novel application of Bayesian optimization to optimize eHMI parameters, advancing automated vehicle communication design.

Findings

Optimal design trade-offs identified via Pareto front

Enhanced trust and safety perception in eHMI designs

Effective balancing of visual and auditory communication elements

Abstract

The absence of a human operator in automated vehicles (AVs) may require external Human-Machine Interfaces (eHMIs) to facilitate communication with other road users in uncertain scenarios, for example, regarding the right of way. Given the plethora of adjustable parameters, balancing visual and auditory elements is crucial for effective communication with other road users. With N=37 participants, this study employed multi-objective Bayesian optimization to enhance eHMI designs and improve trust, safety perception, and mental demand. By reporting the Pareto front, we identify optimal design trade-offs. This research contributes to the ongoing standardization efforts of eHMIs, supporting broader adoption.