论文标题
使用增强状态配方在连接和自动化车辆中检测异常
Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation
论文作者
论文摘要
在本文中，我们提出了一种基于观察者的新型方法，用于连接和自动化车辆（CAVS）中的异常检测方法。所提出的方法利用增强的扩展卡尔曼滤波器（AEKF）来平滑基于非线性汽车跟随运动模型的CAV的传感器读数，并使用时间延迟，其中主体车辆使用了领先的车辆轨迹来检测传感器异常。我们将经典的$χ^2 $故障检测器与拟议的AEKF一起用于异常检测。为了使所提出的模型更适合于现实世界应用，我们考虑了汽车跟随模型的随机通信时间延迟。我们对现实世界中连接的车辆数据进行的实验表明，具有$χ^2 $ -DETECTOR的AEKF可以实现高异常检测性能。
In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $χ^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $χ^2$-detector can achieve a high anomaly detection performance.
