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…
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