# Boreas: A Multi-Season Autonomous Driving Dataset

arXiv: https://arxiv.org/abs/2203.10168 · HTML: https://mlsanity.com/paper/2203.10168/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2022.

## Built on by (most cited first, 30 of 76)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Robust Monocular Depth Estimation under Challenging Conditions](https://mlsanity.com/paper/2308.09711/) (2023)
- [MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty](https://mlsanity.com/paper/2401.12761/) (2024)
- [Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions](https://mlsanity.com/paper/2207.06825/) (2022)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones](https://mlsanity.com/paper/2406.07661/) (2024)
- [Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar](https://mlsanity.com/paper/2405.04662/) (2024)
- [WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments](https://mlsanity.com/paper/2312.15364/) (2023)
- [VBR: A Vision Benchmark in Rome](https://mlsanity.com/paper/2404.11322/) (2024)
- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation](https://mlsanity.com/paper/2303.05194/) (2023)
- [Enabling Visual Recognition at Radio Frequency](https://mlsanity.com/paper/2405.19516/) (2024)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement](https://mlsanity.com/paper/2210.05896/) (2022)
- [Adver-City: Open-Source Multi-Modal Dataset for Collaborative Perception Under Adverse Weather Conditions](https://mlsanity.com/paper/2410.06380/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [RIPE: Reinforcement Learning on Unlabeled Image Pairs for Robust Keypoint Extraction](https://mlsanity.com/paper/2507.04839/) (2025)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition](https://mlsanity.com/paper/2312.10616/) (2023)
- [ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models](https://mlsanity.com/paper/2409.00301/) (2024)
- [Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization](https://mlsanity.com/paper/2305.05301/) (2023)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer](https://mlsanity.com/paper/2404.14034/) (2024)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain](https://mlsanity.com/paper/2311.00660/) (2023)
- [RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments](https://mlsanity.com/paper/2311.03904/) (2023)
- [XLM for Autonomous Driving Systems: A Comprehensive Review](https://mlsanity.com/paper/2409.10484/) (2024)

## Abstract (arXiv)

The Boreas dataset was collected by driving a repeated route over the course of one year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset includes over 350km of driving data featuring a 128-channel Velodyne Alpha Prime lidar, a 360^circ Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. Our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at https://www.boreas.utias.utoronto.ca