  Robust detection of moving vehicles is a critical task for any autonomously
operating outdoor robot or self-driving vehicle. Most modern approaches for
solving this task rely on training image-based detectors using large-scale
vehicle detection datasets such as nuScenes or the Waymo Open Dataset.
Providing manual annotations is an expensive and laborious exercise that does
not scale well in practice. To tackle this problem, we propose a
self-supervised approach that leverages audio-visual cues to detect moving
vehicles in videos. Our approach employs contrastive learning for localizing
vehicles in images from corresponding pairs of images and recorded audio. In
extensive experiments carried out with a real-world dataset, we demonstrate
that our approach provides accurate detections of moving vehicles and does not
require manual annotations. We furthermore show that our model can be used as a
teacher to supervise an audio-only detection model. This student model is
invariant to illumination changes and thus effectively bridges the domain gap
inherent to models leveraging exclusively vision as the predominant modality.
