Change Detection in Automotive Radar based Occupancy Maps using Siamese Networks

In this paper we present a deep learning based approach for detecting changes or deviations in the context of radar based occupancy grid maps. Specifically, we propose a convolutional neural network (CNN) based architecture to identify spatial changes. As a reference map of the environment, we use occupancy maps generated using detections obtained from automotive radar sensors fitted to the corners of a test-vehicle. For the purpose of similarity learning, a siamese architecture is used. The network is trained with occupancy maps of highway and urban scenes captured over a period of time around the city of Wuppertal, Germany, focusing on construction zones on the road. As per the initial evaluations, the siamese network is able to classify images with construction zones as changes from non-changes i.e. images without construction zones.