# The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data

arXiv: https://arxiv.org/abs/2404.01437 · HTML: https://mlsanity.com/paper/2404.01437/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters](https://mlsanity.com/paper/1803.11527/) (2018)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [Point Convolutional Neural Networks by Extension Operators](https://mlsanity.com/paper/1803.10091/) (2018)
- [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](https://mlsanity.com/paper/2005.01456/) (2020)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (2019)
- [2D Car Detection in Radar Data with PointNets](https://mlsanity.com/paper/1904.08414/) (2019)
- [Using Machine Learning to Detect Ghost Images in Automotive Radar](https://mlsanity.com/paper/2007.05280/) (2020)
- [A Multi-Stage Clustering Framework for Automotive Radar Data](https://mlsanity.com/paper/1907.03511/) (2019)
- [Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices](https://mlsanity.com/paper/1905.11987/) (2019)

## Built on by (most cited first, 4 of 4)

- [Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving](https://mlsanity.com/paper/2407.12803/) (2024)
- [Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal](https://mlsanity.com/paper/2603.28224/) (2026)
- [Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath](https://mlsanity.com/paper/2409.06815/) (2024)
- [Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation](https://mlsanity.com/paper/2508.04033/) (2025)

## Abstract (arXiv)

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasoned by their high robustness against meteorological effects, such as rain, snow, or fog, and the radar's ability to measure relative radial velocity differences via the Doppler effect. The cause for these advantages, namely the large wavelength, is also one of the drawbacks of radar sensors. Compared to camera or lidar sensor, a lot more surfaces in a typical traffic scenario appear flat relative to the radar's emitted signal. This results in multi-path reflections or so called ghost detections in the radar signal. Ghost objects pose a major source for potential false positive detections in a vehicle's perception pipeline. Therefore, it is important to be able to segregate multi-path reflections from direct ones. In this article, we present a dataset with detailed manual annotations for different kinds of ghost detections. Moreover, two different approaches for identifying these kinds of objects are evaluated. We hope that our dataset encourages more researchers to engage in the fields of multi-path object suppression or exploitation.