Papers › Improving External Communication of Automated Vehicles Using Bayesian Optimization
Improving External Communication of Automated Vehicles Using Bayesian Optimization
Mark Colley, Pascal Jansen, Mugdha Keskar, Enrico Rukzio
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections