# π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

arXiv: https://arxiv.org/abs/2012.06208 · HTML: https://mlsanity.com/paper/2012.06208/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [On the road to more accurate mobile cellular traffic predictions](https://mlsanity.com/paper/2305.15234/) (2023)
- [Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis](https://mlsanity.com/paper/2403.09571/) (2024)
- [A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning](https://mlsanity.com/paper/2305.09832/) (2023)
- [Cellular Predictions on the Move: What about Data?](https://mlsanity.com/paper/2606.25709/) (2026)

## Abstract (arXiv)

Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated network slices are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose π-ROAD, a deep learning framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. π-ROAD enables operators to proactively instantiate dedicated Emergency Network Slices (ENS) as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within 400~km of a highway in Europe and augmented with publicly available information on related road events. Our results show that π-ROAD successfully detects and classifies non-recurring road events and reduces up to 30% the impact of ENS on already running services.