Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles
This addresses safety risks for autonomous vehicles by providing a scalable testing tool, though it is incremental as it builds on existing simulation methods.
The paper tackles the vulnerability of autonomous vehicles to adversarial attacks by introducing an open-source integrated simulation framework that models multi-domain scenarios targeting perception and communication layers, demonstrating significant performance degradation in a state-of-the-art 3D object detector under realistic conditions.
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.