A fuzzy control system for energy‐efficient wireless devices in the Internet of vehicles

Collotta, Mario; Ferrero, Renato; Giusto, Edoardo; Vakili, Mohammad Ghazi; Grecuccio, Jacopo; Kong, Xiangjie; You, Ilsun

doi:10.1002/int.22353

Cited by 13 publications

(8 citation statements)

References 45 publications

(48 reference statements)

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“…(2) By combining equation (1) and equation ( 2), we obtain (3) According to the geometric relationship between the front and rear wheels of an intelligent vehicle, (4) By combining the above equations, the kinematic model of the intelligent vehicle can be obtained as follows.…”

Section: A Vehicle Kinematics Modelmentioning

confidence: 99%

“…Path tracking control enables intelligent vehicles to drive accurately, quickly, and safely in accordance with a path planned by higher-level control [1]. Many control theories have been developed to perform path tracking control in automated systems designed to drive intelligent vehicles, the most important of which include preview control (a form of model predictive control (MPC)) [2], fuzzy control [3], LQR control [4], PID control [5], and model predictive control [6].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

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In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

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Section: A Vehicle Kinematics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

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“…To better realize anonymous communication in WSNs, whilst considering the fact that WSN energy is insufficient, 27 we propose an IPSadas in this paper. This scheme can guarantee the safe forwarding of messages, realize the confidential transmission of messages in WSN, and provide anonymous communication.…”

Section: Related Workmentioning

confidence: 99%

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Ren

Wang

et al. 2021

Int J of Intelligent Sys

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Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity

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“…It is difficult to deal with emergencies at these two levels, and drivers will cause traffic accidents because they trust the automatic driving system too much. The Internet of Vehicles (IoV) proposed in recent years can meet the needs of higher‐level autonomous vehicles and improve the perception ability of vehicles 2,3 …”

Section: Introductionmentioning

confidence: 99%

“…The Internet of Vehicles (IoV) proposed in recent years can meet the needs of higher-level autonomous vehicles and improve the perception ability of vehicles. 2,3 The concept of the IoV is extended from the Internet of Things (IoT). 4 The early IoT is a centralized platform that collects vehicle driving information in real-time through sensors, on-board units, communication modules, and other equipments deployed on vehicles, which provides a centralized platform for data services for various applications.…”

mentioning

confidence: 99%

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

Jiang

2022

Softw Pract Exp

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With the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto‐encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto‐encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.

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A fuzzy control system for energy‐efficient wireless devices in the Internet of vehicles

Cited by 13 publications

References 45 publications

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

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