UrbanV2X: A Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas

TL;DR

UrbanV2X is a comprehensive multisensory dataset from vehicles and infrastructure in Hong Kong, enabling research on cooperative navigation in dense urban environments with synchronized sensors and benchmarked algorithms.

Contribution

It provides a novel, publicly available multisensory dataset for vehicle-infrastructure cooperative navigation in complex urban areas.

Findings

Benchmarking of navigation algorithms demonstrates the dataset's utility.

High-precision synchronized multisensory data supports advanced research.

The dataset fills a critical gap in urban autonomous vehicle research.

Abstract

Due to the limitations of a single autonomous vehicle, 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. To address this gap, we present UrbanV2X, a comprehensive multisensory dataset collected from vehicles and roadside infrastructure in the Hong Kong C-V2X testbed, designed to support research on smart mobility applications in dense urban areas. Our onboard platform provides synchronized data from multiple industrial cameras, LiDARs, 4D radar, ultra-wideband (UWB), IMU, and high-precision GNSS-RTK/INS navigation systems. Meanwhile, our roadside infrastructure provides LiDAR, GNSS, and UWB measurements. 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. The dataset is publicly available at https://polyu-taslab.github.io/UrbanV2X/.