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

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.

Acoustical and behavioral heuristics for fast interactive sound design

“…In this study, the sounds considered were generated using an additive synthesis method controlled using six parameters with four modalities each. The six parameters correspond respectively to: motor/chord proportion, fundamental frequency, harmonic/noise proportion, number of harmonics, amplitude modulation frequency, and amplitude modulation ratio, see [8,9] for more details. This corresponds to a number of 4 6 = 4096 possible designs.…”

“…The data used for the construction of the behavioral surrogate models was obtained during an interactive multi-objective optimization experiment whose aim is the bi-objective design of an electric vehicle sound presented [8]. This sound should jointly minimize its unpleasantness PLOS ONE and the associated vehicle detection time.…”

“…In order to gather enough data within a reasonable time frame, we choose here to numerically simulate the interactive optimization process for the experiments reported in this paper. For this purpose, we build artificial subject models from previous experimental data issued from an experiment that considered the same sound design problem [ 8 ].…”