Secure Cooperative Localization for Connected Automated Vehicles Based on Consensus

Xia, Xin; Xu, Runsheng; Ma, Jiaqi

doi:10.1109/jsen.2023.3312610

Cited by 11 publications

(4 citation statements)

References 47 publications

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“…In the field of automated vehicles, Neel P. Bhatt and Xin Xia et al (2023) proposed an integrated localization method based on the fusion of an inertial dead reckoning model and 3D LiDAR-based map matching [16]. In their experiments, the Kalman filter is utilized for stability analysis.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

“…In their experiments, the Kalman filter is utilized for stability analysis. Xin Xia and Runsheng Xu et al (2023) utilize the consensus Kalman information filter (CKIF) to merge shared information from connected vehicles, proposing secure cooperative localization for connected automated vehicles (CAVs) [17]. In the literature on SOC prediction using Kalman filtering, Sepasi and Ghorbani et al ( 2014) extended the application of the Kalman filter method to assess the SOC in battery packs [12].…”

Section: Related Work and Research Gapmentioning

confidence: 99%

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Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

Chen,

Li,

Wei

2023

Electronics

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In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 °C to 25 °C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles.

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Section: Related Work and Research Gapmentioning

confidence: 99%

Section: Related Work and Research Gapmentioning

confidence: 99%

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

Chen,

Li,

Wei

2023

Electronics

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“…Motion planning aims at generating a kinematically feasible trajectory with the information of vehicle's location [2] and surrounding objects [3][4][5]. This trajectory will then be used to control the vehicle [6].…”

Section: Introductionmentioning

confidence: 99%

Fast Bidirectional Motion Planning for Self-Driving General N-Trailers Vehicle Maneuvering in Narrow Space

Zhuang,

Shen,

Qian

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Self-driving General N-trailers (GNT) vehicles are one of the future solutions to build intelligent factory due to its flexibility and large load. Maneuvering of GNT vehicle to its destination requires accurate and robust motion planning. But the narrow operating environment causes nonlinear nonconvex constraints which are challenging. Furthermore, the nonholonomic constraints in GNT kinematics elevate the complexity in state space. Therefore, motion planning of GNT vehicle maneuvering in narrow space within a reasonable time and high success rate is a critical problem. This paper proposes a fast bidirectional motion planning algorithm to generate trajectories for GNT vehicles to maneuver in a narrow space. A coarse-to-fine motion planning paradigm has been proposed to balance the robustness and time. In the coarse step, an initial guess is generated through a bidirectional-sampled closed-loop Rapidly-exploring Random Tree, and a spatial-temporal safety corridor has been constructed to convert nonlinear nonconvex constraints to linear convex constraints. In the fine step, an optimal control problem is defined accordingly and solved to obtain feasible trajectory. Four different scenarios have been conducted with forward and reverse GNT vehicle maneuvering in a narrow environment. The results show that the proposed method outperforms state-of-the-art samplingbased and optimization-based motion planning methods.

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“…Therefore, CBA landing technology has attracted widespread attention because it is a precondition for CBA to engage in actual marine combat [1][2][3][4][5]. Furthermore, the emergence of advanced multisensor navigation and localization algorithms [6][7][8] in recent years has provided significant support for research on CBA landing technology. Compared with land-based landing, the glide landing of CBA is extremely complex and difficult because of the limited dimensions of the carrier deck, the impacts of the marine environment and the characteristics of the inverse manoeuvring zone.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

Xing,

2023

Applied Sciences

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Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states.

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Secure Cooperative Localization for Connected Automated Vehicles Based on Consensus

Cited by 11 publications

References 47 publications

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

Fast Bidirectional Motion Planning for Self-Driving General N-Trailers Vehicle Maneuvering in Narrow Space

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

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