Acoustic Non-Line-of-Sight Vehicle Approaching and Leaving Detection

Early detection of occluded moving vehicles at intersections can prevent collisions, but most of the existing sensors only detect objects in sight. Sound propagates around obstacles by means such as reflection and diffraction, allowing passive acoustic sensing of the occluded vehicle. We propose a deep learning-based acoustic non-line-of-sight (NLOS) vehicle detection method. With the direction-of-arrival feature and time-frequency feature calculated from the microphone array data as inputs in image form, we designed and trained the parallel neural network to perceive the direction of the occluded moving vehicle at intersections. Since intelligent vehicles react differently to the approaching and leaving occluded moving vehicle, we further distinguished the occluded moving vehicle's approaching/leaving status. To evaluate the proposed method, we collected data from different locations in the urban environment. The experimental results show that the classification for 6-class intersection traffic conditions reached 96.71%, and the occluded approaching vehicle was detected 1 second before it entered the line of sight, providing additional reaction time for intelligent vehicles. The direction of the occluded moving vehicle is accurately predicted, and the approaching/leaving status is further inferred, providing detailed traffic information for the intelligent vehicles' response decisions. Furthermore, experiments show that the predictions of our method outperform the state-of-the-art acoustic NLOS approach vehicle detection baseline on real-world traffic datasets. Our code and dataset: https://github.com/RST2detection/Acoustic-Occluded-Vehicle-Detection.