# Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?

arXiv: https://arxiv.org/abs/2609.21000 · HTML: https://mlsanity.com/paper/2609.21000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 28 of 28)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [Offboard 3D Object Detection from Point Cloud Sequences](https://mlsanity.com/paper/2103.05073/) (2021)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022)
- [DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds](https://mlsanity.com/paper/2306.06023/) (2023)
- [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection](https://mlsanity.com/paper/2304.12315/) (2023)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics](https://mlsanity.com/paper/2305.01135/) (2023)
- [MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving](https://mlsanity.com/paper/2409.16149/) (2024)
- [MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection](https://mlsanity.com/paper/2308.05988/) (2023)
- [CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar](https://mlsanity.com/paper/2311.01423/) (2023)
- [LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds](https://mlsanity.com/paper/2311.01444/) (2023)
- [Efficient 4D Radar Data Auto-labeling Method using LiDAR-based Object Detection Network](https://mlsanity.com/paper/2407.04709/) (2024)
- [Tracking Multiple Vehicles Using a Variational Radar Model](https://mlsanity.com/paper/1711.03799/) (2017)
- [Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?](https://mlsanity.com/paper/2404.01537/) (2024)
- [SIRA: Scalable Inter-frame Relation and Association for Radar Perception](https://mlsanity.com/paper/2411.02220/) (2024)
- [Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads](https://mlsanity.com/paper/2602.16870/) (2026)
- [Doppler-aware Odometry from FMCW Scanning Radar](https://mlsanity.com/paper/2308.10597/) (2023)
- [An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking](https://mlsanity.com/paper/2002.10098/) (2020)
- [RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving](https://mlsanity.com/paper/2509.16261/) (2025)
- [Radar-Informed 3D Multi-Object Tracking under Adverse Conditions](https://mlsanity.com/paper/2604.13571/) (2026)

## Abstract (arXiv)

Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360deg field of view. Recently, scanning radars have also been shown capable of generating per-azimuth Doppler velocity. In this paper, we investigate whether these Doppler velocity measurements improve spinning radar vehicle detection and tracking performance. For detection, we estimate the ego motion and use it to undo the Doppler range distortion of the radar image before passing it to a network. For tracking, we propose a new way to estimate a per-vehicle velocity and use it as a prior for the tracker's motion model. Since Doppler-enabled spinning radar data is not available in any dataset with ground-truth dynamic object labels, our first contribution is an automatic labelling pipeline that uses an ensemble of fine-tuned off-the-shelf lidar detectors to label all 643 km of the Boreas Road Trip dataset. We then transfer detections to radar, and use over 250 km of vehicle-dense sequences as ground-truth training data. By training and evaluating two state-of-the-art detectors, we show that Doppler undistortion can improve detection accuracy by up to 2.37 points on mean average precision. Furthermore, we show that the Doppler velocity prior can improve tracking accuracy by 13.68 points on multi-object tracking accuracy (MOTA) versus the zero-velocity initialization baseline, while achieving 99.7% of the MOTA obtained using ground-truth velocities as the prior.