mlsanityData to 7 Oct 2026

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

arXiv 2209.14499 ↗First posted 29 Sep 2022cs.CV cs.LG cs.ROcited by 12

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

17 of 25 references.

References · 17

  1. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  4. 2019Objects as Pointscited by 1,172
  5. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
  6. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  7. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  8. 2020CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detectioncited by 159
  9. 2018ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shapecited by 123
  10. 2020FISHING Net: Future Inference of Semantic Heatmaps In Gridscited by 55
  11. 2020RadarNet: Exploiting Radar for Robust Perception of Dynamic Objectscited by 48
  12. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
5 more
  1. 2019A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?cited by 32
  2. 2020Probabilistic Oriented Object Detection in Automotive Radarcited by 27
  3. 2020MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Viewscited by 20
  4. 2020LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusioncited by 18
  5. 2018Probably Unknown: Deep Inverse Sensor Modelling In Radarcited by 16

Built on by

Cited by year

Citing papers per year: 2023: 7; 2024: 4; 2025: 1.

12 papers.

  1. 2023CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perceptioncited by 81
  2. 2023Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Reviewcited by 25
  3. 2023Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Reviewcited by 22
  4. 2024Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehensioncited by 17
  5. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  6. 2023RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Modelscited by 9
  7. 2025DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detectioncited by 6
  8. 2023R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Roboticscited by 4
  9. 2023RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamicscited by 4
  10. 2024Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratioscited by 3
  11. 2024Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception Systemcited by 0
  12. 2024Zero-shot 3D General Obstacle Detection via Multimodal Foundation Models and Geometrycited by 0