Detection of sound from AVAS in urban environments - Demirci, Bircan (My University, 2024)
Functionality of AVAS (Acoustic Vehicle Alert System) faces challenges in urban environments, where background noises may mask AVAS sounds. From a safety perspective, designing AVAS sounds that effectively warn vulnerable road users (without causing extensive environmental noise) is even more crucial in heavy vehicles since they have longer braking distances, greater momentum, and more blind spots than light vehicles. This study aims to contribute to the safety aspects of battery electric trucks (BEV trucks) by exploring their detectability in urban areas and their classification rate, which shows whether they can be distinguishable from cars. In this regard, listening tests were conducted with 51 participants: eight distinct vehicle sounds, five of which belong to a BEV heavy truck, one to an ICE (Internal combustion engine) truck, one to a BEV passenger car, and one to an ICE passenger car. Each vehicle sound was presented both at 10 and 20 km/h. In the first session of the test, participants were tasked with classifying approaching vehicles as either trucks or cars, without additional urban background noise (the equivalent levels are about 45 dB(A)). During the second session, they were tasked with detecting approaching vehicles amidst continuous urban background noise (the equivalent levels are in the range between 57 and 62 dB(A)) and then classifying the detected ones. The results revealed that in the first session of the test, where there was no additional background noise, 50% of the vehicles were correctly classified within the safe zone. The vast majority of the vehicles approaching at 10 km/h were classified within the safe zone, while those approaching at 20 km/h were classified within the unsafe zone. In the second session, with continuous urban background noise, 30% of the vehicles were detected and then correctly classified within safe distances. Unlike the first session, a large portion of the vehicles approaching at 20 km/h were detected and then correctly classified within the safe zone, while those approaching at 10 km/h were detected and then classified within the unsafe zone. While the ICE truck outperformed at both speeds and in both sessions, the accuracy of the BEV truck results varied depending on the session and vehicle speed. Moreover, the accuracy rates of the tasks’ results were mainly affected by whether the AVAS sound was in active mode or not, the modulation of the AVAS sound, and whether the tonal components of the BEV truck were dominant or not. These findings may provide insights into the current and future needs of designing AVAS sounds for electrified trucks.
Keywords: Acoustic Vehicle Alerting Systems, Electric Heavy Vehicles, BEV Trucks, Exterior Vehicle Sound, Pedestrian Safety, Detectability, Listening Experiments