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

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving