# Synthesizing and Identifying Noise Levels in Autonomous Vehicle Camera Radar Datasets

arXiv: https://arxiv.org/abs/2505.00584 · HTML: https://mlsanity.com/paper/2505.00584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [Sparse4D v3: Advancing End-to-End 3D Detection and Tracking](https://mlsanity.com/paper/2311.11722/) (2023)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions](https://mlsanity.com/paper/2210.01346/) (2022)
- [RadSegNet: A Reliable Approach to Radar Camera Fusion](https://mlsanity.com/paper/2208.03849/) (2022)
- [RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network](https://mlsanity.com/paper/2409.04979/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing](https://mlsanity.com/paper/2009.13702/) (2020)

## Built on by (most cited first, 1 of 1)

- [Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing](https://mlsanity.com/paper/2603.17531/) (2026)

## Abstract (arXiv)

Detecting and tracking objects is a crucial component of any autonomous navigation method. For the past decades, object detection has yielded promising results using neural networks on various datasets. While many methods focus on performance metrics, few projects focus on improving the robustness of these detection and tracking pipelines, notably to sensor failures. In this paper we attempt to address this issue by creating a realistic synthetic data augmentation pipeline for camera-radar Autonomous Vehicle (AV) datasets. Our goal is to accurately simulate sensor failures and data deterioration due to real-world interferences. We also present our results of a baseline lightweight Noise Recognition neural network trained and tested on our augmented dataset, reaching an overall recognition accuracy of 54.4% on 11 categories across 10086 images and 2145 radar point-clouds.