Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners

TL;DR

This paper introduces a passive acoustic sensing method for detecting approaching vehicles around blind corners, enabling earlier detection than visual systems and enhancing vehicle safety.

Contribution

It presents a novel microphone array-based approach for classifying approaching vehicles using Direction-of-Arrival features, with systematic environment analysis and real-time detection capabilities.

Findings

Achieves 0.92 accuracy in static environments

Detects approaching vehicles over one second earlier than visual detectors

Maintains 0.84 accuracy during vehicle motion

Abstract

This work proposes to use passive acoustic perception as an additional sensing modality for intelligent vehicles. We demonstrate that approaching vehicles behind blind corners can be detected by sound before such vehicles enter in line-of-sight. We have equipped a research vehicle with a roof-mounted microphone array, and show on data collected with this sensor setup that wall reflections provide information on the presence and direction of occluded approaching vehicles. A novel method is presented to classify if and from what direction a vehicle is approaching before it is visible, using as input Direction-of-Arrival features that can be efficiently computed from the streaming microphone array data. Since the local geometry around the ego-vehicle affects the perceived patterns, we systematically study several environment types, and investigate generalization across these environments. With a static ego-vehicle, an accuracy of 0.92 is achieved on the hidden vehicle classification task. Compared to a state-of-the-art visual detector, Faster R-CNN, our pipeline achieves the same accuracy more than one second ahead, providing crucial reaction time for the situations we study. While the ego-vehicle is driving, we demonstrate positive results on acoustic detection, still achieving an accuracy of 0.84 within one environment type. We further study failure cases across environments to identify future research directions.