There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.

Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets

Cyber–physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs This paper presents a survey on recent advances on security issues of industrial cyber–physical systems (ICPSs) We specifically discuss two typical kinds of attacks, ie, Denial-of-Service (DoS) attack and Deception attack, and present recent results in terms of attack detection, estimation, and control of ICPSs Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods In addition, advantages and disadvantage of various methodologies are also discussed Finally, the paper concludes with some potential future research directions on secure ICPSs

A survey on attack detection, estimation and control of industrial cyber-physical systems.

Control performance of vehicle platooning relies on the information flow topology and quality of wireless communications In this article, we investigate the constant-time-headway-spacing-policy-based vehicle platooning problem, where multiple predecessors’ information is used by the following vehicles and communication impairments, ie, limited communication range and random packet losses, are considered In this article, first, when the leading vehicle moves at a constant speed, we obtain the sufficient and necessary conditions on sampling time, control gains, and internal lag, to ensure the stability of the vehicle platoon based on matrix polynomials’ stability for ideal communications Second, for time-independent homogeneous random packet losses, we provide the upper bound for the loss rate to maintain convergence in expectation by matrix eigenvalue perturbation theory when no input is set for lossy information We also provide sufficient conditions to guarantee mean-square convergence for heterogeneous time-independent random packet losses and show the convergence time for any given accuracy and probability Third, when historically latest information is used for input, the sufficient and necessary conditions are provided to ensure the internal stability and string stability by Markov jump linear system theory Furthermore, we discuss the controller design when no feasible solution exists to guarantee the string stability Extensive numerical results validate our analysis

Stability Analysis of Vehicle Platooning With Limited Communication Range and Random Packet Losses

With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment.

Distributed Learning for Vehicle Routing Decision in Software Defined Internet of Vehicles

This article investigates the distributed sensor deception attack and estimation for a class of platoon-based connected vehicles. In these systems, the information of each vehicle's position and its relative distances with respect to its neighbours plays a crucial role to achieve the desired group performance. When getting deception attacks on such important information, it will result in severe consequences including collision. In order to detect and estimate deception attacks, a longitudinal dynamic model of vehicle platoons with modelling uncertainties, measurement noises and piece-wise constant deception attacks on sensors is first presented. With the consideration of the practical issue that a local vehicle is not able to access to global information, a distributed Kalman filter is proposed to estimate the state information using local output information. Based on the residuals obtained by the distributed Kalman filter, a modified generalized likelihood ratio (GLR) algorithm is proposed to detect and estimate the sensor deception attacks. Finally, simulations with the help of standard Carsim software are provided to verify the effectiveness of the proposed algorithm. Fair comparisons between the proposed method and the standard $\chi ^2$ detector are also presented in order to provide insights for engineering practitioners.

Distributed Deception Attack Detection in Platoon-Based Connected Vehicle Systems

In this article, the position sensor deception attack detection and estimation problem is investigated for a local vehicle in a vehicle platoon. In a platoon system, the position measurement is critical as the distances between neighboring vehicles are relatively small. However, the position measurement of vehicles is usually vulnerable to deception attacks as it relies on external information, such as GPS and environment information from cameras. Therefore, position sensor deception attack detection and estimation should be addressed for local vehicles in a platoon. To deal with this problem, a linearized model is presented to describe the longitudinal dynamics of a local vehicle. Moreover, modeling uncertainties, measurement noises, and piecewise constant deception attacks injected in position measurement are specified along with this model. Based on this model, a scheme based on a modified unbiased finite impulse response (UFIR) estimator is proposed to generate an intermediate estimated value related only to the attack. Then, the deception attack is recovered based on this value through a function fitting strategy. Based on analysis results, simulations are conducted to verify the effectiveness of the proposed attack detection and estimation scheme.

Deception Attack Detection and Estimation for a Local Vehicle in Vehicle Platooning Based on a Modified UFIR Estimator

Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective. Compared to traditional vehicles, CAVs are featured in the increasing number of perception sensors, advanced intra-vehicle communication technologies, capabilities of driving automation and connectivity between single vehicles. These features bring about safety issues which are not encountered in traditional vehicle systems. One main type of these issues is the attack or anomaly launched onto the perception sensors and the communication channels. With such a consideration, this survey summarizes and reviews the existing results on attack/anomaly detection and resilience of CAVs in control frameworks. This paper reviews the results according to the positions at which the attacks/anomalies occur. These positions are divided into three categories, namely, intra-vehicle communication network, perception sensors and inter-vehicle communication network. From this perspective, the recent attack/anomaly detection and resilience results are reviewed according to different positions attacked. After reviewing existing results, some potential research directions and challenges are identified.

A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and Control Perspective

Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platoons Subject to Modeling Uncertainties

In the field of minimally invasive surgery, flexible needles can avoid blood vessels and organs more flexibly compared to rigid needles. One of the main challenges when using flexible needles to reach lesions is planning a suitable path. Due to the non-holonomic characteristic of the flexible needle dynamics and the tissue deformation caused by the needle tip during the insertion, the accessibility and safety of the needle’s states need to be considered in the path planning stage. In this article, we propose an adaptable algorithm by improving the canonical rapidly exploring random trees* (RRT*) algorithm to compute a path for the flexible needle to reach targets in a layered tissue environment. The improved RRT* algorithm that addresses the motion constraints of the flexible needle renders the computed path comparatively smoother and optimal in some approximation sense. In the proposed algorithm, a strategy of adapting some of its parameters for different tissues during the insertion is developed, which improves the safety of surgeries. Moreover, the path cost used in the algorithm takes the potential fields of surrounding obstacles into account, which is used to deal with the influence of the local movement of tissues during the needle puncture process. Simulations are conducted to verify the effectiveness of the proposed algorithm. The results show that the improved RRT* algorithm generates a smooth and safe path which satisfies the motion constraints of the flexible needle in layered tissue environment.

Zhiyang Ju

Papers

Distributed Deception Attack Detection in Platoon-Based Connected Vehicle Systems

Deception Attack Detection and Estimation for a Local Vehicle in Vehicle Platooning Based on a Modified UFIR Estimator

A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and Control Perspective

Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platoons Subject to Modeling Uncertainties

3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues