Evidence Filter of Semantic Segmented Image From Around View Monitor in Automated Parking System

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.