用数字孪生提前发现自动驾驶的隐藏故障，提升安全验证效率。
ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems
- 构建高保真仿真环境，整合车、路、感、故障模型进行压力测试。
- 通过强化学习探索罕见极端场景，发现物理测试难捕捉的失效模式。
- 开源框架支持大规模安全测试，适合自动驾驶研发与验证团队使用。
自动驾驶系统仍面临由罕见且不可预测的极端情况引发的安全关键故障，传统测试难以覆盖。本文提出自动驾驶数字孪生（ADDT）框架，一个高保真仿真平台，旨在主动识别隐藏缺陷、评估实时性能并验证安全性。ADDT融合真实驾驶环境、车辆动力学、传感器行为及故障状态的数字模型，支持在多样恶劣条件下进行可扩展的、场景丰富的压力测试。通过强化学习驱动的自适应探索机制，能够发现物理道路测试中常被遗漏的异常故障模式。该框架实现从被动调试到主动仿真验证的转变，为自动驾驶安全工程提供更严格透明的方法。为加速行业应用并推动整体安全提升，ADDT已作为开源软件发布，为开发者提供可访问、可扩展的大规模综合安全测试工具。
原文摘要 · Abstract (English)
Autonomous driving systems continue to face safety-critical failures, often triggered by rare and unpredictable corner cases that evade conventional testing. We present the Autonomous Driving Digital Twin (ADDT) framework, a high-fidelity simulation platform designed to proactively identify hidden faults, evaluate real-time performance, and validate safety before deployment. ADDT combines realistic digital models of driving environments, vehicle dynamics, sensor behavior, and fault conditions to enable scalable, scenario-rich stress-testing under diverse and adverse conditions. It supports adaptive exploration of edge cases using reinforcement-driven techniques, uncovering failure modes that physical road testing often misses. By shifting from reactive debugging to proactive simulation-driven validation, ADDT enables a more rigorous and transparent approach to autonomous vehicle safety engineering. To accelerate adoption and facilitate industry-wide safety improvements, the entire ADDT framework has been released as open-source software, providing developers with an accessible and extensible tool for comprehensive safety testing at scale.
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