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。本文针对自动驾驶车辆中的目标跟踪攻击设计展开研究。假设目标车辆采用基于检测的目标跟踪系统，该系统依赖多个传感器，并利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被攻击车辆所估计的物体状态，使其陷入危险工况。为实现这一目标，在期望的时间范围内，通过对与目标物体对应的部分或全部传感器输出进行精心扰动，构建了一个随机模型预测控制问题来计算扰动序列，其中对扰动施加硬约束，并对物体状态施加概率机会约束。机会约束确保攻击成功所需的条件以预设概率得到满足。随后通过合理假设，得到可计算求解的线性SMPC程序。在仿真环境下以自适应巡航控制系统为例进行验证，成功生成了序列化攻击，导致被攻击车辆陷入包括碰撞在内的危险驾驶场景。