DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

TL;DR

DRIVE introduces a scalable digital twin framework for cooperative intelligent transportation systems, enabling real-time interaction, large-scale city simulations, and facilitating machine learning development for autonomous vehicles.

Contribution

A novel, modular digital twin architecture for C-ITS that supports real-time queries and large-scale city simulations with moderate computational resources.

Findings

Supports city-wide experimentation with realistic mobility traces

Provides real-time, bidirectional interaction with external agents

Facilitates development and training of ML-based C-ITS solutions

Abstract

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.