A Data Driven Approach to the Development and Evaluation of Acoustic Electric Vehicle Alerting Systems for Vision Impaired Pedestrians

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Safe-D National UTC

The number of electric vehicles on the road increases exponentially every year. Due to the quieter nature of these vehicles when operating at low speeds, there is significant concern that pedestrians and bicyclists will be at increased risk of vehicle collisions. This research explores the detectability of six electric vehicle acoustic additive sounds produced by two sound dispersion techniques: (1) using the factory approach versus (2) an exciter transducer-based system. Detectability was initially measured using on-road participant tests and was then replicated in a high-fidelity immersive reality lab. Results were analyzed through both mean detection distances and pedestrian probability of detection. This research aims to verify the lab environment in order to allow for a broader range of potential test scenarios, more repeatable tests, and faster test sessions. Along with pedestrian drive-by tests, supplemental experiments were conducted to evaluate stationary vehicle acoustics, 10 and 20 km/h drive by acoustics, and interior acoustics of each additive sound.

Electric vehicles, Pedestrians, Vision impaired, Detection, Additive sounds