标题： 你的自动驾驶车辆安全吗？ 理解电磁信号注入攻击对交通场景感知的威胁 显示英文标题

标题： Is Your Autonomous Vehicle Safe? Understanding the Threat of Electromagnetic Signal Injection Attacks on Traffic Scene Perception

摘要： 自动驾驶车辆依赖基于摄像头的感知系统来理解其驾驶环境并做出关键决策，从而确保车辆安全行驶。 然而，一种被称为电磁信号注入攻击（ESIA）的重大威胁可以扭曲这些摄像头捕获的图像，导致错误的人工智能决策，并可能危及自动驾驶车辆的安全。 尽管ESIA具有严重的影响，但对其在各种复杂驾驶场景中对人工智能模型鲁棒性影响的理解仍然有限。 为解决这一问题，我们的研究分析了不同模型在ESIA下的性能，揭示了它们对攻击的脆弱性。 此外，由于获取真实世界攻击数据存在挑战，我们开发了一种新的ESIA模拟方法，并为不同的驾驶场景生成了模拟攻击数据集。 我们的研究提供了一个全面的模拟和评估框架，旨在提高更强大人工智能模型和安全智能系统的开发，最终为各个领域更安全、更可靠的技术进步做出贡献。 显示英文摘要

摘要： 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.