Improving External Communication of Automated Vehicles Using Bayesian Optimization

要旨

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.

著者
Mark Colley
Ulm University, Ulm, Germany
Pascal Jansen
Ulm University, Ulm, Baden-Württemberg, Germany
Mugdha Keskar
Ulm University, Germany, Ulm, Germany
Enrico Rukzio
University of Ulm, Ulm, Germany
DOI

10.1145/3706598.3714187

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714187

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Automated Vehicles and XR

Annex Hall F205
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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