Decision making in advanced driver assistance systems involves in general the estimated trajectories of the surrounding objects. Multiple object tracking refers to the process of estimating in real time these trajectories, leveraging for this purpose sensors to detect the objects. This paper deals with devising attacks on object tracking in automated vehicles. The vehicle is assumed to have a detection-based object tracking system that relies on multiple sensors and uses an estimator such as a Kalman filter for sensor fusion and state estimation. The attack goal is to modify the object's state estimated by the victim vehicle to put the vehicle in an unsafe situation. This goal is achieved by judiciously perturbing some or all of the sensor outputs corresponding to the object of interest over a desired horizon. A stochastic model predictive control (SMPC) problem is formulated to compute the sequence of perturbations, whereby hard constraints on the perturbations and probabilistic chance constraints on the object's state are imposed. The chance constraints ensure that some desired conditions for a successful attack are satisfied with a prespecified probability. Reasonable assumptions are then made to obtain a computationally tractable linear SMPC program. The approach is demonstrated on an adaptive cruise control system in a simulation environment, where successful sequential attacks are generated, leading the victim vehicle into dangerous driving situations including collisions.

翻译：高级驾驶辅助系统中的决策通常涉及对周围物体轨迹的估计。多目标跟踪是指利用传感器探测物体，实时估计这些轨迹的过程。本文研究针对自动驾驶车辆目标跟踪的攻击设计。假设车辆采用基于检测的目标跟踪系统，该系统依赖多传感器，并使用卡尔曼滤波器等估计器进行传感器融合与状态估计。攻击目标是通过修改受害车辆估计的物体状态，使车辆陷入不安全情境。为此，我们通过有选择性地在期望时间范围内，对与目标物体相关的部分或全部传感器输出进行合理扰动。建立了一个随机模型预测控制（SMPC）问题来计算扰动序列，其中对扰动的硬约束以及物体状态的概率机会约束被施加。机会约束确保攻击成功所需的某些期望条件以预设概率得到满足。随后通过合理假设，得到计算可行的线性SMPC程序。在仿真环境中的自适应巡航控制系统上验证了该方法，生成了成功的序列攻击，导致受害车辆陷入包括碰撞在内的危险驾驶情境。