Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network utilizes temporally accumulated data from multiple RADAR sensors to detect dynamic obstacles and compute their orientation in a top-down bird's-eye view (BEV). The network also regresses drivable free space to detect unclassified obstacles. Our DNN is the first of its kind to utilize sparse RADAR signals in order to perform obstacle and free space detection in real time from RADAR data only. The network has been successfully used for perception on our autonomous vehicles in real self-driving scenarios. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.

翻译：检测障碍物对于安全高效的自动驾驶至关重要。为此，我们提出NVRadarNet，一种利用车载雷达传感器检测动态障碍物与可行驶自由空间的深度神经网络（DNN）。该网络利用多雷达传感器的时序累积数据，在俯视鸟瞰图（BEV）中检测动态障碍物并计算其朝向。同时，网络回归可行驶自由空间以检测未分类障碍物。本DNN是首个仅利用稀疏雷达信号实现实时障碍物与自由空间检测的同类网络。该网络已在真实自动驾驶场景中成功用于感知任务，其在嵌入式GPU上的运行速度超过实时要求，并展现出跨地理区域的良好泛化能力。