An Advanced Framework for Ultra-Realistic Simulation and Digital Twinning for Autonomous Vehicles

TL;DR

BlueICE is a novel, flexible simulation framework that enables ultra-realistic digital twins for autonomous vehicles by decoupling platform dependencies and integrating diverse simulation tools.

Contribution

The paper introduces BlueICE, a new architecture that enhances simulation compatibility, accuracy, and customization for autonomous vehicle testing environments.

Findings

Successfully created digital twins for indoor and outdoor testbeds.

Demonstrated improved simulation compatibility and flexibility.

Validated the framework's effectiveness in real-world autonomous vehicle testing.

Abstract

Simulation is a fundamental tool in developing autonomous vehicles, enabling rigorous testing without the logistical and safety challenges associated with real-world trials. As autonomous vehicle technologies evolve and public safety demands increase, advanced, realistic simulation frameworks are critical. Current testing paradigms employ a mix of general-purpose and specialized simulators, such as CARLA and IVRESS, to achieve high-fidelity results. However, these tools often struggle with compatibility due to differing platform, hardware, and software requirements, severely hampering their combined effectiveness. This paper introduces BlueICE, an advanced framework for ultra-realistic simulation and digital twinning, to address these challenges. BlueICE's innovative architecture allows for the decoupling of computing platforms, hardware, and software dependencies while offering researchers customizable testing environments to meet diverse fidelity needs. Key features include containerization to ensure compatibility across different systems, a unified communication bridge for seamless integration of various simulation tools, and synchronized orchestration of input and output across simulators. This framework facilitates the development of sophisticated digital twins for autonomous vehicle testing and sets a new standard in simulation accuracy and flexibility. The paper further explores the application of BlueICE in two distinct case studies: the ICAT indoor testbed and the STAR campus outdoor testbed at the University of Delaware. These case studies demonstrate BlueICE's capability to create sophisticated digital twins for autonomous vehicle testing and underline its potential as a standardized testbed for future autonomous driving technologies.