Vehicle-to-Everything (V2X) datasets for Machine Learning-based Predictive Quality of Service

Skocaj, Marco; Di Cicco, Nicola; Zugno, Tommaso; Boban, Mate; Blumenstein, Jiri; Prokes, Ales; Mikulasek, Tomas; Vychodil, Josef; Mikhaylov, Konstantin; Tornatore, Massimo; Degli-Esposti, Vittorio (2023-09-01)
 
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https://doi.org/10.1109/MCOM.004.2200723

Skocaj, Marco
Di Cicco, Nicola
Zugno, Tommaso
Boban, Mate
Blumenstein, Jiri
Prokes, Ales
Mikulasek, Tomas
Vychodil, Josef
Mikhaylov, Konstantin
Tornatore, Massimo
Degli-Esposti, Vittorio
Institute of Electrical and Electronics Engineers
01.09.2023

M. Skocaj et al., "Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service," in IEEE Communications Magazine, vol. 61, no. 9, pp. 106-112, September 2023, doi: 10.1109/MCOM.004.2200723

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doi:https://doi.org/10.1109/MCOM.004.2200723
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Abstract

We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean.

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