Adaptive Cruise Control (ACC) is a widely used driver assistance feature for maintaining desired speed and safe distance to the leading vehicles. This paper evaluates the security of the deep neural network (DNN) based ACC systems under stealthy perception attacks that strategically inject perturbations into camera data to cause forward collisions. We present a combined knowledge-and-data-driven approach to design a context-aware strategy for the selection of the most critical times for triggering the attacks and a novel optimization-based method for the adaptive generation of image perturbations at run-time. We evaluate the effectiveness of the proposed attack using an actual driving dataset and a realistic simulation platform with the control software from a production ACC system and a physical-world driving simulator while considering interventions by the driver and safety features such as Automatic Emergency Braking (AEB) and Forward Collision Warning (FCW). Experimental results show that the proposed attack achieves 142.9x higher success rate in causing accidents than random attacks and is mitigated 89.6% less by the safety features while being stealthy and robust to real-world factors and dynamic changes in the environment. This study provides insights into the role of human operators and basic safety interventions in preventing attacks.

翻译：自适应巡航控制（ACC）是一种广泛使用的驾驶辅助功能，用于保持期望车速并与前车保持安全距离。本文评估了基于深度神经网络（DNN）的ACC系统在隐蔽感知攻击下的安全性，此类攻击通过策略性地向摄像头数据注入扰动来引发前方碰撞。我们提出了一种知识与数据相结合的混合驱动方法，用于设计上下文感知策略以选择攻击触发的最关键时机，并提出了一种新颖的基于优化的方法，用于在运行时自适应生成图像扰动。我们利用实际驾驶数据集和包含量产ACC系统控制软件及物理世界驾驶模拟器的逼真仿真平台，评估了所提攻击的有效性，同时考虑了驾驶员干预以及自动紧急制动（AEB）和前方碰撞预警（FCW）等安全功能。实验结果表明，与随机攻击相比，所提攻击引发事故的成功率高出142.9倍，且被安全功能缓解的程度低89.6%，同时具备隐蔽性，并对现实因素和环境动态变化具有鲁棒性。本研究为操作人员及基本安全干预在防御攻击中的作用提供了见解。