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.…
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Taxonomy
MethodsConvolution · RoIPool · Softmax · Region Proposal Network · Faster R-CNN
