Evaluating and ensuring the adversarial robustness of autonomous driving (AD) systems is a critical and unresolved challenge. This paper introduces MetAdv, a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation by tightly integrating virtual simulation with physical vehicle feedback. At its core, MetAdv establishes a hybrid virtual-physical sandbox, within which we design a three-layer closed-loop testing environment with dynamic adversarial test evolution. This architecture facilitates end-to-end adversarial evaluation, ranging from high-level unified adversarial generation, through mid-level simulation-based interaction, to low-level execution on physical vehicles. Additionally, MetAdv supports a broad spectrum of AD tasks, algorithmic paradigms (e.g., modular deep learning pipelines, end-to-end learning, vision-language models). It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments, with built-in compatibility for commercial platforms such as Apollo and Tesla. A key feature of MetAdv is its human-in-the-loop capability: besides flexible environmental configuration for more customized evaluation, it enables real-time capture of physiological signals and behavioral feedback from drivers, offering new insights into human-machine trust under adversarial conditions. We believe MetAdv can offer a scalable and unified framework for adversarial assessment, paving the way for safer AD.

翻译：评估并确保自动驾驶系统的对抗鲁棒性是一项关键且尚未解决的挑战。本文介绍了MetAdv，一种新型对抗测试平台，通过将虚拟仿真与物理车辆反馈紧密集成，实现了真实、动态且交互式的评估。其核心在于构建了一个混合虚拟-物理沙箱，在此框架内设计了一个具有动态对抗测试演进的三层闭环测试环境。该架构支持端到端的对抗评估，涵盖从高层统一对抗生成、中层基于仿真的交互，到底层物理车辆执行的全过程。此外，MetAdv支持广泛的自动驾驶任务与算法范式（如模块化深度学习流水线、端到端学习、视觉-语言模型），提供灵活的3D车辆建模及仿真与物理环境间的无缝切换，并内置了对Apollo、Tesla等商业平台的兼容性。MetAdv的一个关键特性是其人在回路能力：除了支持灵活的环境配置以实现更定制化的评估外，还能实时采集驾驶员的生理信号与行为反馈，为对抗条件下的人机信任研究提供新视角。我们相信MetAdv能为对抗评估提供一个可扩展的统一框架，为更安全的自动驾驶铺平道路。