# UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception

arXiv: https://arxiv.org/abs/2510.23478 · HTML: https://mlsanity.com/paper/2510.23478/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024)
- [V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2403.16034/) (2024)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions](https://mlsanity.com/paper/2407.07462/) (2024)
- [Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction](https://mlsanity.com/paper/2401.14325/) (2024)

## Built on by (most cited first, 15 of 15)

- [UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios](https://mlsanity.com/paper/2511.11252/) (2025)
- [V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views](https://mlsanity.com/paper/2604.02710/) (2026)
- [DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration](https://mlsanity.com/paper/2601.15260/) (2026)
- [Real-Time Evaluation of Autonomous Systems under Adversarial Attacks](https://mlsanity.com/paper/2605.03491/) (2026)
- [Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization](https://mlsanity.com/paper/2606.07708/) (2026)
- [RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2606.31895/) (2026)
- [When the City Teaches the Car: Label-Free 3D Perception from Infrastructure](https://mlsanity.com/paper/2603.16742/) (2026)
- [FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views](https://mlsanity.com/paper/2605.29997/) (2026)
- [RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/2606.29867/) (2026)
- [CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization](https://mlsanity.com/paper/2606.31219/) (2026)
- [Latency-Aware Digital Twin-Assisted Cooperative Perception for Autonomous Vehicles](https://mlsanity.com/paper/2607.09070/) (2026)
- [CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception](https://mlsanity.com/paper/2607.16154/) (2026)
- [MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management](https://mlsanity.com/paper/2607.22654/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)
- [SV2V-RSim: A Comprehensive Benchmark for Self-Selective V2V Cooperative Perception with Near-Realistic Data](https://mlsanity.com/paper/2609.32863/) (2026)

## Abstract (arXiv)

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset. We provide comprehensive evaluations using state-of-the-art cooperative perception methods and publicly release the codebase, dataset, HD map, and a digital twin of the complete data collection environment.