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

arXiv: https://arxiv.org/abs/2209.14499 · HTML: https://mlsanity.com/paper/2209.14499/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://mlsanity.com/paper/1812.02781/) (2018)
- [FISHING Net: Future Inference of Semantic Heatmaps In Grids](https://mlsanity.com/paper/2006.09917/) (2020)
- [RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects](https://mlsanity.com/paper/2007.14366/) (2020)
- [4D-Net for Learned Multi-Modal Alignment](https://mlsanity.com/paper/2109.01066/) (2021)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)
- [Probabilistic Oriented Object Detection in Automotive Radar](https://mlsanity.com/paper/2004.05310/) (2020)
- [MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views](https://mlsanity.com/paper/2006.05518/) (2020)
- [LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion](https://mlsanity.com/paper/2010.00731/) (2020)
- [Probably Unknown: Deep Inverse Sensor Modelling In Radar](https://mlsanity.com/paper/1810.08151/) (2018)

## Built on by (most cited first, 13 of 13)

- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension](https://mlsanity.com/paper/2405.12821/) (2024)
- [RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models](https://mlsanity.com/paper/2309.06619/) (2023)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics](https://mlsanity.com/paper/2308.15368/) (2023)
- [DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection](https://mlsanity.com/paper/2508.12330/) (2025)
- [R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics](https://mlsanity.com/paper/2308.15039/) (2023)
- [Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios](https://mlsanity.com/paper/2412.05707/) (2024)
- [Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System](https://mlsanity.com/paper/2404.16548/) (2024)
- [Zero-shot 3D General Obstacle Detection via Multimodal Foundation Models and Geometry](https://mlsanity.com/paper/2408.12322/) (2024)
- [TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems](https://mlsanity.com/paper/2606.13891/) (2026)

## 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.