Autonomous vehicles rely on camera-based perception systems to comprehend their driving environment and make crucial decisions, thereby ensuring vehicles to steer safely. However, a significant threat known as Electromagnetic Signal Injection Attacks (ESIA) can distort the images captured by these cameras, leading to incorrect AI decisions and potentially compromising the safety of autonomous vehicles. Despite the serious implications of ESIA, there is limited understanding of its impacts on the robustness of AI models across various and complex driving scenarios. To address this gap, our research analyzes the performance of different models under ESIA, revealing their vulnerabilities to the attacks. Moreover, due to the challenges in obtaining real-world attack data, we develop a novel ESIA simulation method and generate a simulated attack dataset for different driving scenarios. Our research provides a comprehensive simulation and evaluation framework, aiming to enhance the development of more robust AI models and secure intelligent systems, ultimately contributing to the advancement of safer and more reliable technology across various fields.

本研究针对电磁信号注入攻击（ESIA）对自动驾驶汽车感知系统的影响进行了深入分析，揭示了现有AI模型在复杂驾驶场景下的脆弱性。我们创新性地开发了一种ESIA模拟方法，生成了用于不同驾驶场景的模拟攻击数据集，旨在提升AI模型的鲁棒性，推动更安全的智能系统发展。

你的自动驾驶汽车安全吗？理解电磁信号注入攻击对交通场景感知的威胁