# DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/2007.03680 · HTML: https://mlsanity.com/paper/2007.03680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model](https://mlsanity.com/paper/2207.00166/) (2022)

## Abstract (arXiv)

In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.