构建可模拟车端感知与通信攻击的综合仿真框架
Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles
- 统一配置文件驱动多模态仿真器协同运行
- 支持激光雷达与车联网消息等多类攻击
- 适配主流自动驾驶软件栈，适合安全测试
自动驾驶汽车依赖复杂的感知与通信系统，易受对抗攻击威胁。现有仿真框架普遍缺乏对多域攻击场景的全面建模能力。本文提出一个开源集成仿真框架，可生成针对自动驾驶车辆感知层与通信层的对抗攻击。该框架具备高保真物理环境、交通流动态及车联网络建模能力，通过单一配置文件统一调度多个仿真器。支持对激光雷达数据的多种感知级攻击，以及车联网消息篡改和GPS欺骗等通信级威胁。通过与ROS 2集成，实现与第三方自动驾驶软件栈的无缝兼容。实验评估了生成攻击对先进3D目标检测器的影响，在真实条件下显现出显著性能下降。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a scalable and safe environment for robustness testing, existing frameworks typically lack comprehensive supportfor modeling multi-domain adversarial scenarios. This paper introduces a novel, open-source integrated simulation framework designed to generate adversarial attacks targeting both perception and communication layers of AVs. The framework provides high-fidelity modeling of physical environments, traffic dynamics, and V2X networking, orchestrating these components through a unified core that synchronizes multiple simulators based on a single configuration file. Our implementation supports diverse perception-level attacks on LiDAR sensor data, along with communication-level threats such as V2X message manipulation and GPS spoofing. Furthermore, ROS 2 integration ensures seamless compatibility with third-party AV software stacks. We demonstrate the framework's effectiveness by evaluating the impact of generated adversarial scenarios on a state-of-the-art 3D object detector, revealing significant performance degradation under realistic conditions.
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