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<span style="font-weight: 600;">The UrbanV2X Datasets</span>
</h1>
<p class="desc">
Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas.
</p>
</header>
<article>
<div class="clearfix">
<p><br></p>
<!-- <picture style="text-align: center;">
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</div>
<p><br></p>
<p style="text-align: justify;">
Due to the limitations of single autonomous vehicles, Cellular Vehicle-to-Everything (C-V2X) technology opens a new window for achieving fully autonomous driving through sensor information sharing. However, real-world datasets supporting vehicle–infrastructure cooperative navigation in complex urban environments remain rare.
</p>
<p style="text-align: justify;">
To address this gap, we present UrbanV2X, a comprehensive multisensory dataset collected from both vehicles and roadside infrastructure in the Hong Kong C-V2X testbed, designed to support research on smart mobility applications in dense urban areas.
</p>
<p style="text-align: justify;">
Our onboard platform provides time-synchronized data from multiple industrial cameras, LiDARs, 4D radar, UWB, IMU, and high-precision GNSS-RTK/INS navigation systems. Meanwhile, our roadside infrastructure provides LiDAR, GNSS, and UWB measurements.
</p>
<p style="text-align: justify;">
The entire vehicle–infrastructure platform is synchronized using the Precision Time Protocol (PTP), with sensor calibration data provided. We also benchmark various navigation algorithms to evaluate the collected cooperative data.
</p>
<!-- After the paper is accepted, we will release all the datasets and keep the dataset maintenance. -->
<br>
The contributions of our work can be summarized as follows:
</p>
<ul>
<li style="text-align: justify;"> UrbanV2X Dataset and Sensor Integration: Our <a href="/sensors">sensor platform</a> integrating various sensors like multiple event cameras, GNSS/INS, 4D Radar, LiDAR and sky-pointing cameara with roadside infrastructure, including GNSS, LiDAR, and UWB.
</li>
<!-- <li style="text-align: justify;">
ALl sensors are <a href="/UrbanV2X/calibration">well-calibrated</a> and temporally synchronized at the hardware level, with recording data simultaneously.
</li> -->
<li style="text-align: justify;"> UrbanV2X collects comprehensive data from both urban canyons and open areas. These <a href="/UrbanV2X/download">sequences</a> are recorded under various conditions. Our dataset and benchmark results are publicly available on our website.
</li>
<!-- <li style="text-align: justify;"> We present a comprehensive <a href="/UrbanV2X/benchmark">benchmark</a> that evaluates existing state-of-the-art SLAM algorithms of various
sensor modalities and analyzes their limitations.
</li> -->
</ul>
<!-- <p style="text-align: justify;">
We hope that we can make some contributions for the development of event-based vision, especially event-based multi-sensor fusioin for autonomous driving.
<br>
The visualization of each sequence is available in <a href="/UrbanV2X/download">Download section</a> and <a href="https://www.bilibili.com/video/BV1Km4y157KC/?spm_id_from=333.999.0.0&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a>.
<br>
If you have any suggestions or questions, do not hesitate to propose an issue to our <a href="https://polyu-taslab.github.io/UrbanV2X">Github Repository</a>.
</p> -->
<p><br></p>
</div>
<!-- News -->
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<h2>News</h2>
<div class="table-responsive">
<table class="table table-sm table-borderless">
<tr>
<th scope="row">December 2, 2023</th>
<td>
We release our GNSS-RTK/INS gt and M8T/F9P GNSS at <a href="/UrbanV2X/download">Download section</a>.
</td>
</tr>
<tr>
<th scope="row">November 28, 2023</th>
<td>
Our work has been accepted by IEEE Transactions on Intelligent Vehicles!
</td>
</tr>
<tr>
<th scope="row">November 21, 2023</th>
<td>
Calibration results and rosbag are avaliable at <a href="/UrbanV2X/calibration">Calibration page</a>.
</td>
</tr>
<tr>
<th scope="row">November 19, 2023</th>
<td>
We release our sequences at <a href="/UrbanV2X/download">Download section</a>.
</td>
</tr>
<tr>
<th scope="row">November 07, 2023</th>
<td>
The preprint version is available at <a href="https://arxiv.org/abs/2311.02327">arXiv</a>.
</td>
</tr>
<tr>
<th scope="row">October 31, 2023</th>
<td>
Watch our video presentation on <a href="https://www.bilibili.com/video/BV1pN411s79g/?spm_id_from=333.999.list.card_archive.click&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a> or <a href="https://youtu.be/Q1F9M_DZLws">Youtube</a>.
</td>
</tr>
<tr>
<th scope="row">August 28, 2023</th>
<td>
We finish the evaluation of UrbanV2X using various LiDAR SLAM (<a href="https://space.bilibili.com/499377825/channel/collectiondetail?sid
</td>
</tr>
<tr>
<th scope="row">August 22, 2023</th>
<td>
We complete the collection of all sequences (<a href="https://www.bilibili.com/video/BV1Km4y157KC/?share_source=copy_web&vd_source=f5ac2a23210d1b4b7b4aa5e374feae10">bilibili visulization</a>).
</td>
</tr>
<tr>
<th scope="row">June 2, 2023</th>
<td>
Driver code and time synchronization of event cameras are now available (<a href="https://github.com/arclab-hku/Event_based_VO-VIO-SLAM/tree/main/driver_code/dv-ros-master/script">Code</a>, <a href="https://www.bilibili.com/video/BV168411o7BJ/?spm_id_from=333.999.0.0&vd_source=a88e426798937812a8ffc1a9be5a3cb7">Bilibili</a>).
</td>
</tr>
<tr>
<th scope="row">June 1, 2023</th>
<td>
UrbanV2X Datasets goes live!
</td>
</tr>
</table>
</div>
</div> -->
<!-- Selected papers -->
<div class="publications">
<h2>BibTeX</h2>
Please cite the following publication when using this benchmark in an academic context:
<pre style="background-color: #f8f9fa; padding: 10px; border: 1px solid #ddd; border-radius: 5px; overflow-x: auto;">
@INPROCEEDINGS{11423762,
author={Qin, Qijun and Zhang, Ziqi and Zhong, Yihan and Huang, Feng and Liu, Xikun and Hu, Runzhi and Chen, Hang and Hu, Wei and Su, Dongzhe and Zhang, Jun and Ng, Hoi-Fung and Wen, Weisong},
booktitle={2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)},
title={UrbanV2X: A Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas},
year={2025},
volume={},
number={},
pages={815-822},
keywords={Global navigation satellite system;Simultaneous localization and mapping;Laser radar;Navigation;Benchmark testing;Cameras;Synchronization;Ultra wideband radar;Autonomous vehicles;Vehicle-to-everything;Multi-sensor Fusion;Road Side;SLAM;Autonomous Driving;Dataset},
doi={10.1109/ITSC60802.2025.11423762}}
</pre>
<h2></h2>
<ol class="bibliography"><li>
<!-- _layouts/bib.html -->
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<!-- <div class="col-sm-2 abbr"><abbr class="badge">RA-L</abbr></div> -->
<!-- 上面这个RAL显示好像有点问题 -->
<!-- Entry bib key -->
<div id="UrbanV2X2023" class="col-sm-8">
<!-- Author -->
<div class="paper" style="text-align: justify;">Qin, Q., Zhang, Z., Zhong, Y., Huang, F., Liu, X., Hu, R., Chen, H., Hu, W., Su, D., Zhang, J., Ng, H.-F., & Wen, W. (2025).
UrbanV2X: A Multisensory Vehicle-Infrastructure
Dataset for Cooperative Navigation in Urban Areas
</div>
<!-- Title -->
<!-- <div class="title" style="text-align: justify;">UrbanV2X: An Event-Centric Multisensory Driving Dataset for SLAM.</div> -->
<!-- Journal/Book title and date -->
<!-- <div class="periodical" style="text-align: justify;">
IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 407-416, 2023.
</div> -->
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<!-- <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://arxiv.org/abs/2311.02327" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">arXiv</a>
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</div>
<!-- Hidden abstract block -->
<div class="abstract hidden">
<p style="text-align: justify;">
Leveraging multiple sensors enhances complex environmental perception and increases resilience to varying luminance conditions and high-speed motion patterns, achieving precise localization and mapping.
This paper proposes, UrbanV2X, an event-centric multisensory dataset containing 81 sequences and covering over 200 km of various challenging driving scenarios including high-speed motion, repetitive scenarios, dynamic objects, etc.
UrbanV2X provides data from two sets of stereo event cameras with different resolutions (640×480, 346×260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs,
two consumer-level GNSS receivers, and an onboard IMU.
Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system.
All sensors are well-calibrated and temporally synchronized at the hardware level, with recording data simultaneously.
We additionally evaluate several state-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM and identifying their limitations.
<!-- The full dataset can be found at \url{https://polyu-taslab.github.io/UrbanV2X/}. -->
</p>
</div>
</div>
</div>
</li></ol>
</div>
<br>
<div class="Other resources">
<h2>Other resources</h2>
<p style="text-align: justify;">
Some tools for pre-processing the dataset
and HKU event-based handheld & drone dataset are available at <a href="https://github.com/arclab-hku/Event_based_VO-VIO-SLAM" target="_blank" rel="noopener noreferrer">here</a>.
</p>
</div>
<!-- License -->
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<h2>License</h2>
<p>
This work is released under <a href="https://www.gnu.org/licenses/" target="_blank" rel="noopener noreferrer">GPLv3</a> license.
For commercial inquires, please contact Dr. Wen Weisong(welson.wen@polyu.edu.hk).
</p>
</div>
<br>
<br>
<!-- Acknowledgements -->
<div class="Acknowledgement">
<h2>Acknowledgement</h2>
<p style="text-align: justify;">
The authors thank Yuteng Wang, Shaoting Qiu from PolyU, Jiashi Feng, Alpamys Urtay and Siqiao from ASTRI for their kind support in this data evaluation.
</p>
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