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：检测障碍对于安全、高效自主驾驶至关重要。 为此,我们展示了NVRadarNet,这是一个利用汽车RADAR传感器探测动态障碍和可驾驶自由空间的深层神经网络(DNN),该网络利用多个RADAR传感器的时间累积数据探测动态障碍,并用自上而下鸟眼观(BEV)计算其方向。网络还倒退了可驾驶的自由空间,以探测未分类障碍。我们的DNN是首次使用稀有的RADAR信号,以便从RADAR数据中实时进行障碍和自由空间探测的同类网络。网络成功地用于在真实的自驾驶情景下方对我们的自主飞行器进行感知。网络在嵌入的GPU上运行比实时速度快,并展示了跨地理区域的良好通用。
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