Existing ADAS solutions for car environmental awareness (cameras, LiDAR, ultrasonic, etc.) typically require targets to be in a clear line of sight from the sensor. The target must be illuminated by some source of energy, so systems are affected by dust, weather, lighting, and obstacles. We address those limitations using a passive acoustic solution that “listens” to the environment. It can hear potential targets around corners or out of sight over a distance, providing early warning that supplements and improves other ADAS systems. We aim to detect a variety of road participant including sirens, as well as approaching vehicles, bicycles and even pedestrians. We discuss use cases and challenges, present an inexpensive reference architecture based on automotive grade components, and report on the state of development with initial validation results.

Authors: Sieracki, Jeff; Boehm, Matthias; Patki, Prachi; Caggiano, Matthew; Noll, Markus
Affiliations: Reality AI, Columbia, MD, USA; Infineon Technologies AG, Neubiberg, Germany(See document for exact affiliation information.)
AES Conference: 2022 AES International Conference on Automotive Audio (June 2022)
Paper Number: 15
Publication Date: June 8, 2022 Import into BibTeX
Subject: Automotive Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=21816

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