Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar

Abstract

Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal patterns of human motion, i.e. the micro-Doppler signature. In this paper we propose a spatio-temporal Convolutional Neural Network (CNN) capable of detecting VRUs in cluttered radar data. The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model. The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar. Lidar provides accurate ranging ground truth, while camera information helps distinguish between VRUs and background. Experimental evaluation shows that the CNN model has superior detection performance compared to classical techniques. Moreover, the model trained with imperfect, weak supervision labels outperforms the one trained with a limited number of perfect, hand-annotated labels. Finally, the proposed method has excellent scalability due to the low cost of automatic annotation.