LAUSR

An Around View Monitor (AVM) is widely used as one of the perception sensors for automated parking systems. By applying semantic segmentation based on a deep learning approach, the AVM can detect two essential elements for automated parking systems: slot marking and obstacles. However, the perception based on the deep learning approach in the AVM has certain limitations such as occlusion of the ego-vehicle region, distortion of 3D objects, and environmental noise. We overcome the problems by proposing an evidence filter that improves the detection performance based on evidence theory and a Simultaneous Localization and Mapping (SLAM) algorithm. The proposed algorithm is composed of three parts: the semantic segmentation of the AVM image, confidence modeling based on evidence theory, and evidence SLAM. Semantic segmentation classifies the grids in the AVM image into three states: slot marking, freespace, and obstacle. The grids with these three states are modeled by a confidence model based on evidence theory. Finally, the states of the grids around the ego-vehicle are accumulated and estimated by the evidence SLAM. The proposed filter was evaluated by experiments in real parking-lot environments.