A Deep Automotive Radar Detector Using the RaDelft Dataset

Abstract

The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchronized lidar data are used as ground truth to train a neural network (NN) with only radar data as input. To this end, the novel, large-scale, real-life, and multisensor RaDelft dataset has been recorded using a demonstrator vehicle in different locations in the city of Delft, The Netherlands. The dataset, as well as the documentation and example code, is publicly available for those researchers in the field of automotive radar or machine perception. The proposed data-driven detector can generate lidar-like point clouds (PCs) using only radar data from a high-resolution system, which preserves the shape and size of extended targets. The results are compared against conventional constant false alarm rate (CFAR) detectors as well as variations of the method to emulate the available approaches in the literature, using the probability of detection, the probability of false alarm, and the Chamfer distance (CD) as performance metrics. Moreover, an ablation study was carried out to assess the impact of Doppler and temporal information on detection performance. The proposed method outperforms different baselines in terms of CD, achieving a reduction of 77% against conventional CFAR detectors and 28% against the modified state-of-the-art deep learning (DL)-based approaches.