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

2024-05-02

arXiv.org

AI Summary

To address poor compatibility and coordination among heterogeneous simulation tools in autonomous driving simulation—arising from platform, hardware, and software heterogeneity—this paper proposes BlueICE, a novel decoupled framework. BlueICE separates dependencies across computation platforms, hardware, and software layers; implements containerized deployment; integrates ROS/DDS hybrid communication bridging; and introduces cross-simulator I/O synchronization orchestration. This enables coordinated scheduling of heterogeneous simulators (e.g., CARLA, IVRESS) and supports real-time digital twin mapping with dynamic calibration. Evaluated on two testbeds—the ICAT indoor facility and the STAR campus—the framework demonstrates high-fidelity dynamic scene modeling and closed-loop testing capabilities. It exhibits strong reusability and scalability, establishing a standardized, ultra-realistic simulation and digital twin foundation for autonomous driving development.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Addresses compatibility issues in autonomous vehicle simulators
• Enables ultra-realistic simulation and digital twinning
• Provides customizable testing environments for diverse fidelity needs

Innovation

• Decouples platform, hardware, and software dependencies
• Uses containerization for cross-system compatibility
• Unifies communication bridge for seamless tool integration