With their robustness to adverse weather conditions and ability to measure
speeds, radar sensors have been part of the automotive landscape for more than
two decades. Recent progress toward High Definition (HD) Imaging radar has
driven the angular resolution below the degree, thus approaching laser scanning
performance. However, the amount of data a HD radar delivers and the
computational cost to estimate the angular positions remain a challenge. In
this paper, we propose a novel HD radar sensing model, FFT-RadNet, that
eliminates the overhead of computing the range-azimuth-Doppler 3D tensor,
learning instead to recover angles from a range-Doppler spectrum. FFT-RadNet is
trained both to detect vehicles and to segment free driving space. On both
tasks, it competes with the most recent radar-based models while requiring less
compute and memory. Also, we collected and annotated 2-hour worth of raw data
from synchronized automotive-grade sensors (camera, laser, HD radar) in various
environments (city street, highway, countryside road). This unique dataset,
nick-named RADIal for "Radar, Lidar et al.", is available at
this https URL

本文介绍了一种基于 FFT-RadNet 的新型高清雷达感知模型，该感知模型不需要计算范围 - 方位 - 多普勒 3D 张量，而使用范围 - 多普勒频谱进行角度恢复；通过该模型实现了车辆检测和驾驶空间分割。同时，文中还介绍了一组同步的汽车级传感器（相机、激光雷达、高清雷达）所采集且已经进行标注的数据集（RADIal）