Monocular Blind Spot Estimation with Occupancy Grid Mapping

Abstract

We present a low-cost method for detecting blind spots in front of the ego vehicle. In low visibility conditions, blind spot estimation is crucial to avoid the risk of pedestrians or vehicles appearing suddenly. However, most blind spot estimation methods require expensive range sensors or neural networks trained with data measured by them. Our method only uses a monocular camera throughout all phases from training to inference, since it is cheaper and more versatile. We assume that a blind spot is a depth discontinuity region. Occupancy probabilities of these regions are integrated using the occupancy grid mapping algorithm. Instead of using range sensors, we leverage the self-supervised monocular depth estimation method for the occupancy grid mapping. 2D blind spot labels are created from occupancy grids and a blind spot estimation network is trained using these labels. Our experiments show quantitative and qualitative performance and demonstrate an ability to learn with arbitrary videos.