# PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence

arXiv: https://arxiv.org/abs/2507.14211 · HTML: https://mlsanity.com/paper/2507.14211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Simulation-based reinforcement learning for real-world autonomous driving](https://mlsanity.com/paper/1911.12905/) (2019)
- [End-to-End Simulation of 5G mmWave Networks](https://mlsanity.com/paper/1705.02882/) (2017)
- [Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation](https://mlsanity.com/paper/1305.0124/) (2013)
- [QoS Evaluation and Prediction for C-V2X Communication in Commercially-Deployed LTE and Mobile Edge Networks](https://mlsanity.com/paper/2002.07883/) (2020)
- [Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation](https://mlsanity.com/paper/2103.03819/) (2021)
- [On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks](https://mlsanity.com/paper/2104.11785/) (2021)
- [Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies](https://mlsanity.com/paper/2212.10343/) (2022)
- [Investigating Value of Information in Future Vehicular Communications](https://mlsanity.com/paper/1907.10124/) (2019)
- [Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison](https://mlsanity.com/paper/2202.00719/) (2022)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [QoS Prediction for 5G Connected and Automated Driving](https://mlsanity.com/paper/2107.05000/) (2021)
- [Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3](https://mlsanity.com/paper/2203.05449/) (2022)
- [An ns-3 Implementation of a Bursty Traffic Framework for Virtual Reality Sources](https://mlsanity.com/paper/2103.04609/) (2021)
- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches](https://mlsanity.com/paper/2302.11966/) (2023)
- [System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving](https://mlsanity.com/paper/2201.11432/) (2022)
- [Federated Reinforcement Learning to Optimize Teleoperated Driving Networks](https://mlsanity.com/paper/2410.02312/) (2024)

## Abstract (arXiv)

Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. Hence, PQoS is extremely useful for automotive applications such as teleoperated driving, which poses strict constraints in terms of latency and reliability. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.