  In this paper, we address the limitations of traditional constant false alarm
rate (CFAR) target detectors in automotive radars, particularly in complex
urban environments with multiple objects that appear as extended targets. We
propose a data-driven radar target detector exploiting a highly efficient 2D
CNN backbone inspired by the computer vision domain. Our approach is
distinguished by a unique cross sensor supervision pipeline, enabling it to
learn exclusively from unlabeled synchronized radar and lidar data, thus
eliminating the need for costly manual object annotations. Using a novel
large-scale, real-life multi-sensor dataset recorded in various driving
scenarios, we demonstrate that the proposed detector generates dense,
lidar-like point clouds, achieving a lower Chamfer distance to the reference
lidar point clouds than CFAR detectors. Overall, it significantly outperforms
CFAR baselines detection accuracy.
