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Journal Articles Design Science Year : 2022

An interactive bi-objective optimisation process to guide the design of electric vehicle warning sounds

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Abstract

Abstract Electric vehicles (EVs) are very quiet at low speed, which can be hazardous for pedestrians, especially visually impaired people. It is now mandatory (since mid-2019 in Europe) to add external warning sounds, but poor sound design can lead to noise pollution, and consequently annoyance. Moreover, it is possible that EVs are not sufficiently detectable in urban areas because of the masking effect from the background noise. In this paper, we propose a method for the design of warning sounds that takes into account both detectability and unpleasantness. The method implements a multiobjective interactive genetic algorithm (IGA) for the optimisation of the characteristics of synthesised sounds. An experiment is proposed to a first panel of participants in order to define a set of Pareto efficient sounds. At the individual level, sounds obtained with the IGA are compared to different sound design proposals. Results show that the quality of the sounds designed by the IGA method is comparable to those provided by a sound designer. From the sounds of the Pareto set, a design recommendation method based on the probability distributions of the sounds’ characteristics is proposed. An external validation with a second panel of participants shows that these recommended sounds constitute relevant trade-offs when compared to other design proposals.
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Dates and versions

hal-03865223 , version 1 (22-11-2022)

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Tom Souaille, Jean-François Petiot, Nicolas Misdariis, Mathieu Lagrange. An interactive bi-objective optimisation process to guide the design of electric vehicle warning sounds. Design Science, 2022, 8, pp.e26. ⟨10.1017/dsj.2022.18⟩. ⟨hal-03865223⟩
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