Issue |
Acta Acust.
Volume 8, 2024
|
|
---|---|---|
Article Number | 27 | |
Number of page(s) | 20 | |
Section | Virtual Acoustics | |
DOI | https://doi.org/10.1051/aacus/2024025 | |
Published online | 20 August 2024 |
Scientific Article
Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems
Division of Applied Acoustics, Chalmers University of Technology, 41296 Gothenburg, Sweden
* Corresponding author: leon.mueller@chalmers.se
Received:
2
February
2024
Accepted:
11
June
2024
Electric vehicles (EVs) typically emit little noise at low driving speeds, which increases the risk of accidents for vulnerable road users such as pedestrians. To reduce this risk, regulations demand that newly sold EVs have to be equipped with an acoustic vehicle alerting system (AVAS), which radiates artificial warning sounds. Developing AVAS sounds that provide a sufficient warning capability while limiting traffic noise annoyance requires laboratory listening experiments; such experiments need accurate auralization methods. Even though several auralization tools are already established in the research field, those frameworks require additional data to simulate EVs. This paper presents an electric vehicle auralization toolchain combined with an open-access database, including AVAS measurements, synthesis algorithms, and numerically calculated sound source directivities for three different electric passenger cars. The auralization method was validated numerically and in a listening experiment, comparing simulated EV passages to binaural in-situ recordings. The results of this perceptual validation indicate that stimuli generated with the presented method are perceived as slightly less plausible than in-situ recordings and that they result in a similar distribution of annoyance ratings but a higher perceived vehicle velocity compared to the reference recordings.
Key words: Auralization / AVAS / Electric vehicles / Perceptual evaluation / Plausibility
© The Author(s), Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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