论文标题
对先进驾驶员援助系统的战略性安全关键攻击
Strategic Safety-Critical Attacks Against an Advanced Driver Assistance System
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
A growing number of vehicles are being transformed into semi-autonomous vehicles (Level 2 autonomy) by relying on advanced driver assistance systems (ADAS) to improve the driving experience. However, the increasing complexity and connectivity of ADAS expose the vehicles to safety-critical faults and attacks. This paper investigates the resilience of a widely-used ADAS against safety-critical attacks that target the control system at opportune times during different driving scenarios and cause accidents. Experimental results show that our proposed Context-Aware attacks can achieve an 83.4% success rate in causing hazards, 99.7% of which occur without any warnings. These results highlight the intolerance of ADAS to safety-critical attacks and the importance of timely interventions by human drivers or automated recovery mechanisms to prevent accidents.
