The growing adoption of electric vehicles, known for their quieter operation compared to internal combustion engine vehicles, raises concerns about their detectability, particularly for vulnerable road users. To address this, regulations mandate the inclusion of exterior sound signals for electric vehicles, specifying minimum sound pressure levels at low speeds. These synthetic exterior sounds are often used in noisy urban environments, creating the challenge of enhancing detectability without introducing excessive noise annoyance. This study investigates the design of synthetic exterior sound signals that balance high noticeability with low annoyance. An audiovisual experiment with 14 participants was conducted using 15 virtual reality scenarios featuring a passing car. The scenarios included various sound signals, such as pure, intermittent, and complex tones at different frequencies. Two baseline cases, a diesel engine and only tyre noise, were also tested. Participants rated sounds for annoyance, noticeability, and informativeness using 11-point ICBEN scales. The findings highlight how psychoacoustic sound quality metrics predict annoyance ratings better than conventional sound metrics, providing insight into optimising sound design for electric vehicles. By improving pedestrian safety while minimising noise pollution, this research supports the development of effective and user-friendly exterior sound standards for electric vehicles.

翻译：电动汽车相较于内燃机汽车运行更为安静，其日益普及引发了对其可探测性的担忧，尤其对弱势道路使用者而言。为此，法规要求电动汽车配备外部声音信号，并规定了低速行驶时的最小声压级。这些合成外部声音通常用于嘈杂的城市环境，这带来了在增强可探测性的同时避免引入过度噪声干扰的挑战。本研究探讨了如何设计兼具高显著性与低干扰性的合成外部声音信号。通过一项包含14名参与者的视听实验，在15个虚拟现实场景中模拟了车辆经过的情景。这些场景涵盖了多种声音信号，包括不同频率的纯音、间歇音和复合音。同时测试了两种基准情况：柴油发动机声音和仅轮胎噪声。参与者使用11点ICBEN量表对声音的干扰性、显著性和信息性进行了评分。研究结果表明，心理声学音质指标比传统声音指标更能预测干扰性评分，这为优化电动汽车声音设计提供了见解。通过提升行人安全性并最小化噪声污染，本研究支持开发有效且用户友好的电动汽车外部声音标准。