# Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

arXiv: https://arxiv.org/abs/2212.10343 · HTML: https://mlsanity.com/paper/2212.10343/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [QoS Evaluation and Prediction for C-V2X Communication in Commercially-Deployed LTE and Mobile Edge Networks](https://mlsanity.com/paper/2002.07883/) (2020)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling](https://mlsanity.com/paper/2110.06382/) (2021)

## Built on by (most cited first, 6 of 6)

- [Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches](https://mlsanity.com/paper/2302.11966/) (2023)
- [QoS prediction in radio vehicular environments via prior user information](https://mlsanity.com/paper/2402.17689/) (2024)
- [Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction](https://mlsanity.com/paper/2504.01947/) (2025)
- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)
- [The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems](https://mlsanity.com/paper/2607.18267/) (2026)
- [MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management](https://mlsanity.com/paper/2607.22654/) (2026)

## Abstract (arXiv)

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.