MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving

• URL: http://arxiv.org/abs/2508.06534v3
• Date: Tue, 04 Nov 2025 01:23:14 GMT
• Title: MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving
• Authors: Aishan Liu, Jiakai Wang, Tianyuan Zhang, Hainan Li, Jiangfan Liu, Siyuan Liang, Yilong Ren, Xianglong Liu, Dacheng Tao,
• Abstract summary: MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
• Score: 85.04826012938642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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