Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners

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-ofsight. 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.

Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

“…The parameters of the surrounding environment are obtained through the on-board camera, to serve automatic driving. [13][14][15][16] As required, the machine vision perception platform for automatic vehicle driving is equipped with 6 cameras, which are arranged according to three different functions: front view, measuring view and rear view. The front camera mainly captures the image information of the road in front of the vehicle, obtains the front environment according to the obtained information, and empties the information for the system's early warning and vehicle control through in-depth analysis.…”

GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

“…A dataset specifically focusing on radar perception is presented in [35]. More recently, several datasets covering novel sensing modalities have been made publicly available focusing on acoustic detection [36] and dynamic vision sensors (DVS) [37], [38]. In the same spirit, our work contributes ground penetrating radars as a new sensing modality to dataset-driven perception research.…”

C-AVDI: Compressive Measurement-Based Acoustic Vehicle Detection and Identification

“…The frequency-domain features extracted from vehicle audio signals are used with a support vector machine (SVM) classifier to identify and classify vehicles and their associated parameters in [6], and used to detect vehicles for the purpose of collision avoidance in non-line-of-sight situations in [7].…”

“…[5] MFCC KNN /ANN [6] DFT SVM [7] STFT SVM [8] MFCC MLP [9] MFCC CNN /PCP/STE /LSTM [10] Mod-PCEN SNN [11] GCC RANSAC [12] GCC RANSAC [13] DWT LR architecture in which information acquired at sub-Nyquist rates from different frequency bands is used in a range of applications without prior signal reconstruction.…”

“…Various low-cost, low-complexity VDI techniques have been proposed, with acoustic vehicle detection and identification (AVDI) in particular being the subject of wide-ranging research [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The low price and simple installation process of AVDI systems make them an attractive alternative to other more expensive and difficult-to-install VDI systems, such as video camera-, radar-, or induction loop coilbased systems.…”