An Advanced Framework for Ultra-Realistic Simulation and Digital Twinning for Autonomous Vehicles
arxiv(2024)
摘要
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.
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