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

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arxiv 2105.14370 v1 pith:6YLGMTYY submitted 2021-05-29 cs.CV cs.LG

Deng Yongqiang , Wang Dengjiang , Cao Gang , Ma Bing , Guan Xijia , Wang Yajun , Liu Jianchao , Fang Yanming
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Li Juanjuan
This is my paper · ORCID
classification cs.CVcs.LG
keywords roadsidedatasetbaai-vanjeechallengingdatahighwayobjectannotations
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A digital twin of a real intersection can generate LiDAR training data that matches the target location, and a detector trained on it reported 4.8% higher car AP than a model trained on real data, though with more syn...

  2. DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications

    cs.CV 2025-01 reject novelty 5.0 of 10

    A teacher-student pipeline trains a SECOND detector on clustering and heuristic pseudo-labels, but the claimed parity with human-annotated training is unsubstantiated.

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