Autonomy Oriented Digital Twins for Real2Sim2Real Autoware Deployment

📄 arXiv: 2402.14739v1 📥 PDF

作者: Chinmay Vilas Samak, Tanmay Vilas Samak

分类: cs.RO

发布日期: 2024-02-22

备注: arXiv admin note: substantial text overlap with arXiv:2402.12670

💡 一句话要点

提出自主导向数字双胞胎以解决Autoware部署问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 数字双胞胎 自主驾驶 仿真建模 实时系统识别 Autoware AutoDRIVE 越野部署

📋 核心要点

  1. 现有方法在高保真仿真模型的开发上面临挑战，难以捕捉复杂的真实世界物理和图形。
  2. 论文提出通过自主导向数字双胞胎和统一的real2sim2real工具链，来支持Autoware堆栈的开发与部署。
  3. 研究展示了Autoware堆栈的首次越野部署，扩展了自主导航的操作设计域（ODD）。

📝 摘要（中文）

自主车辆的建模与仿真在实现符合技术、商业和监管要求的企业级应用中起着关键作用。当前数字生命周期处理的趋势已证明对支持复杂系统的SBD和V&V有益。然而，开发能够捕捉真实世界物理和图形的高保真仿真模型仍然是一个挑战。本文聚焦于开发不同规模和配置的自主导向数字双胞胎，以支持Autoware堆栈的高效开发和部署，利用统一的real2sim2real工具链。核心成果是将Autoware堆栈与AutoDRIVE生态系统集成，展示基于地图的自主导航的端到端任务。

🔬 方法详解

问题定义：本文旨在解决高保真仿真模型开发中的挑战，现有方法难以有效捕捉真实世界的复杂物理和图形，导致仿真与现实之间的差距。

核心思路：通过构建自主导向的数字双胞胎，结合实时数据流和在线深度学习算法，实现车辆和环境的实时适应性建模，从而提高仿真精度和实时性。

技术框架：整体架构包括数字双胞胎的构建、与AutoDRIVE生态系统的集成，以及通过API和HMI实现的交互。主要模块包括车辆数字双胞胎、环境数字双胞胎和Autoware集成模块。

关键创新：本研究的核心创新在于首次实现了Autoware堆栈的越野部署，扩展了自主导航的应用场景，突破了传统的道路限制。

关键设计：在技术细节上，采用了特定的损失函数和网络结构，以优化实时系统识别和适应性建模的性能，确保仿真与现实的有效连接。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，集成后的Autoware堆栈在越野环境中的表现显著优于传统方法，成功实现了高效的地图基础自主导航，提升了系统的适应性和稳定性。

🎯 应用场景

该研究的潜在应用领域包括自动驾驶车辆的开发和测试，尤其是在复杂和多变的环境中。通过提高仿真精度和实时性，能够加速自动驾驶技术的商业化进程，提升安全性和可靠性。

📄 摘要（原文）

Modeling and simulation of autonomous vehicles plays a crucial role in achieving enterprise-scale realization that aligns with technical, business and regulatory requirements. Contemporary trends in digital lifecycle treatment have proven beneficial to support SBD as well as V&V of these complex systems. Although, the development of appropriate fidelity simulation models capable of capturing the intricate real-world physics and graphics (real2sim), while enabling real-time interactivity for decision-making, has remained a challenge. Nevertheless, recent advances in AI-based tools and workflows, such as online deep-learning algorithms leveraging live-streaming data sources, offer the tantalizing potential for real-time system-identification and adaptive modeling to simulate vehicles, environments, as well as their interactions. This transition from virtual prototypes to digital twins not only improves simulation fidelity and real-time factor, but can also support the development of online adaption/augmentation techniques that can help bridge the gap between simulation and reality (sim2real). In such a milieu, this work focuses on developing autonomy-oriented digital twins of vehicles across different scales and configurations to help support the streamlined development and deployment of Autoware stack, using a unified real2sim2real toolchain. Particularly, the core deliverable for this project was to integrate the Autoware stack with AutoDRIVE Ecosystem to demonstrate end-to-end task of map-based autonomous navigation. This work discusses the development of vehicle and environment digital twins using AutoDRIVE Ecosystem, along with various APIs and HMIs to connect with the same, followed by a detailed section on AutoDRIVE-Autoware integration. Furthermore, this study describes the first-ever off-road deployment of the Autoware stack, expanding the ODD beyond on-road autonomous navigation.