mlsanityData to 14 Oct 2026

BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China

arXiv 2105.14370 ↗First posted 29 May 2021cs.CV cs.LGcited by 19

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Abstract (arXiv)

As the roadside perception plays an increasingly significant role in the Connected Automated Vehicle Highway(CAVH) technologies, there are immediate needs of challenging real-world roadside datasets for bench marking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. In this paper, we firstly introduce a challenging BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations. By providing a real complex urban intersections and highway scenes, we expect the BAAI-VANJEE roadside dataset will actively assist the academic and industrial circles to accelerate the innovation research and achievement transformation in the field of intelligent transportation in big data era.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 29 May 2021.

Builds on

10 of 14 references.

References · 10

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  4. 2015Microsoft COCO Captions: Data Collection and Evaluation Servercited by 1,462
  5. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  6. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  7. 2018The ApolloScape Open Dataset for Autonomous Driving and its Applicationcited by 190
  8. 2020A2D2: Audi Autonomous Driving Datasetcited by 190
  9. 2019A*3D Dataset: Towards Autonomous Driving in Challenging Environmentscited by 65
  10. 2019D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarioscited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 7; 2025: 6; 2026: 1.

19 papers.

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  1. 2024RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perceptioncited by 38
  2. 2022A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperationcited by 21
  3. 2022Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Surveycited by 14
  4. 2023PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completioncited by 14
  5. 2024Collaborative Perception Datasets in Autonomous Driving: A Surveycited by 11
  6. 2024Open-Vocabulary Object Detection via Language Hierarchycited by 10
  7. 2025R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perceptioncited by 9
  8. 2025Collaborative Perception Datasets for Autonomous Driving: A Reviewcited by 8
  9. 2023Robust Roadside Perception: an Automated Data Synthesis Pipeline Minimizing Human Annotationcited by 6
  10. 2024Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Surveycited by 5
  11. 2024Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Surveycited by 4
  12. 2024Domain Adaptation for Large-Vocabulary Object Detectorscited by 2
7 more
  1. 2024Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detectioncited by 2
  2. 2025UrbanTwin: Synthetic Roadside LiDAR Datasetscited by 2
  3. 2025A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtractioncited by 1
  4. 2026RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perceptioncited by 1
  5. 2023Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applicationscited by 0
  6. 2025UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluationcited by 0
  7. 2025High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perceptioncited by 0