# Spatio-temporal Dynamics of Cellular V2X Communication in Dense Vehicular Networks

arXiv: https://arxiv.org/abs/1906.08634 · HTML: https://mlsanity.com/paper/1906.08634/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic](https://mlsanity.com/paper/2107.05664/) (2021)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Towards Learning Generalizable Driving Policies from Restricted Latent Representations](https://mlsanity.com/paper/2111.03688/) (2021)
- [Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks](https://mlsanity.com/paper/2103.07636/) (2021)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment](https://mlsanity.com/paper/2609.20578/) (2026)

## Abstract (arXiv)

Cellular Vehicle-to-everything (C-V2X) communication is a major V2X solution proposed and developed by the 3rd Generation Partnership Project (3GPP). Our previous work has studied scalability aspects of C-V2X and demonstrated its potential for accommodating large numbers of vehicles in dense vehicular scenarios. However, existing studies in the scientific literature mostly have a network-level approach to the problem and do not assess the temporal and spatial dynamics of C-V2X networks in heavy network load situations. In this work we shed light on the spatio-temporal characteristics of these networks and investigate the effectiveness of the congestion control algorithm in dense vehicular ad-hoc networks (VANETs) in terms of settling time, stability, and reliability to be employed for the purpose of safety-critical vehicular applications, where latency plays a major role.