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

Ju, Zhiyang; Zhang, Hui; Li, Xiang; Chen, Xiaoguang; Han, Jinpeng

doi:10.1109/tiv.2022.3186897

Cited by 111 publications

(24 citation statements)

References 150 publications

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“…and using the scalar k as an indirect measure for how much room there is for the next h(x) improvement, given the current h(x) and 𝛽(x). Then the following SOS programming problem, max k and coefficients of 𝛽(x),s i (x),i=i x +1,…,2i x +i x i u k subject to (32) and ( 35), (36) where h(x) is fixed and 𝛼 is known, can be used to synthesize a new controller u = 𝛽(x). Since h(x) is fixed, the constraints of ( 22) and ( 28) are not necessary for this SOS programming.…”

Section: Controller Synthesis With a Fixed Polynomial Cbfmentioning

confidence: 99%

“…We apply our proposed method to generate a CBF for the supervisory controller in automotive lane keeping, which is a popular research area in advanced driver assistance systems and autonomous vehicles [32][33][34][35]. It is important to improve the resilience for automotive lane keeping [36], and CBF-based supervisory control can enhance the system resilience. We also provide explanations and visualizations for the constructed CBF, and illustrate the effectiveness of the lane keeping supervisory controller in simulations with a 14-degree-of-freedom vehicle dynamics model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational synthesis of control barrier functions with applications in automotive lane keeping supervisory control

Duan

Zeng

2023

IET Control Theory & Appl

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The control barrier function (CBF) provides a good tool to design supervisory controllers for safety‐critical systems. However, constructing a CBF for a general nonlinear system is a challenging task. This paper presents a general CBF computational synthesis approach for nonlinear affine systems with state constraints and input constraints using sum‐of‐squares (SOS) programming. System dynamics and state constraints are approximated using polynomial functions, and the input constraints are approximated using linear functions. The CBF synthesis algorithm is presented step‐by‐step, and tricks to deal with potential numerical issues during the SOS optimizations are discussed. The proposed algorithm is applied to an automotive lane keeping application to synthesize a CBF, and a supervisory control based on this CBF is designed. The effectiveness and resilience of this CBF‐based supervisory control against disturbance are illustrated in simulation with a 14‐degree‐of‐freedom vehicle dynamics model. Results show that this CBF‐based supervisory control can improve the resilience of nominal controllers in automotive lane keeping applications.

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Section: Controller Synthesis With a Fixed Polynomial Cbfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational synthesis of control barrier functions with applications in automotive lane keeping supervisory control

Duan

Zeng

2023

IET Control Theory & Appl

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“…Regarding the highly nonlinear dynamics, uncertainties and disturbances of QUAVs, we present a T3-FS based controller in this paper and new adaptation rules are derived to ensure the stability and robustness. Besides, since there always exist external disturbances for dynamical systems especially for QUAVs, the design of a controller whose structure is robust against disturbances is momentous [24,25]. To this end, integrating a robust control method with T3-FSs results in a highly robust control technique against parametric and non-parametric uncertainties which is studied in this paper.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Robust state and output feedback prescribed performance interval type‐3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

Elhaki

Shojaei

Mohammadzadeh

2022

IET Control Theory & Appl

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This paper presents a novel adaptive reinforcement learning control method with interval type-3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high-performance closed-loop system, a high-gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov-based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques.

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“…Connected and automated vehicles (CAVs) are recognized to enhance vehicle safety while improving highway capacity and reducing fuel consumption [7][8][9]. Especially with the continuous improvement of vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communication technology, a kind of multi-vehicle cooperation system is proposed to guide the coordination movement of the involved vehicles.…”

Section: Motivationmentioning

confidence: 99%

An optimal trajectory planning for automated on‐ramp merging

Liang

Guan

Liu

et al. 2022

IET Intelligent Trans Sys

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This paper proposes an optimal trajectory planning model for automated on‐ramp merging. The minimum cost path based on the optimal control theory is selected as the optimal trajectory of the on‐ramp merging vehicle, which could avoid potential collisions and satisfy the kinematic constraints on the vehicle motion. Moreover, an optimal control strategy is presented to solve the trajectory planning problem of the facilitating vehicle. An analytical closed‐form solution is derived by using the Hamiltonian analysis and the path information of the merging vehicle. Particularly, the proposed planning process also considers the lateral movement of the on‐ramp merging, which is more efficient in the application. Owing to the location and time that the on‐ramp vehicle merges into the mainline can be obtained endogenously by its planning process, the model has high adaptation to various on‐ramp environments. Solutions to the above two optimal methods are implemented in a model predictive control framework to cope with possible external disturbances. Several numerical simulations and PreScan‐Simulink Co‐Simulation illustrate the effectiveness of the proposed model. Furthermore, this methodology is compared with a typical CACC strategy to demonstrate its potential to reduce fuel consumption, as well as improve passenger comfort.

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

Cited by 111 publications

References 150 publications

Computational synthesis of control barrier functions with applications in automotive lane keeping supervisory control

Computational synthesis of control barrier functions with applications in automotive lane keeping supervisory control

Robust state and output feedback prescribed performance interval type‐3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

An optimal trajectory planning for automated on‐ramp merging

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