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Machines
Special Issues
Modeling, Estimation, Control, and Decision for Intelligent Vehicles
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Modeling, Estimation, Control, and Decision for Intelligent Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5880

Special Issue Editors

Dr. Zejiang Wang

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Interests: vehicle dynamics and control; intelligent transportation; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhao Liang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 119077, Singapore
Interests: intelligent vehicle control; connected and automated vehicles; advanced driver assistance system
Dr. Xingyu Zhou

E-Mail Website
Guest Editor
Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712, USA
Interests: autonomous driving automotive control; adaptive and robust control; control systems engineering
Dr. Zhenwu Fang

E-Mail Website
Guest Editor Assistant
Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
Interests: vehicle dynamics and control; advanced driver assistance system; human-machine shared control

Special Issue Information

Dear Colleagues,

In the future of transportation and mobility, the automation and intelligence level of vehicles will become the main factors for improving traffic safety, traffic efficiency, and driving comfort. The high-precision dynamics modeling and state estimation of intelligent vehicles, human-likeness, and real-time performance of decision-making control are core technologies for the development of intelligent vehicles. This Special Issue aims to provide up-to-date research concepts, theoretical findings, and practical solutions for modeling, estimation, control, and decisions for intelligent vehicles, which could enhance the automation and intelligence levels of vehicles. Topics of interest include but are not limited to the following:

  • Personalized Decision and Control for Autonomous Driving
  • End-To-End (E2E) Autonomous Driving
  • Software-Defined Vehicle for Intelligent Vehicles
  • Vehicle-To-Everything (V2X) and Cellular V2X (C-V2X) Communications
  • Cooperative Vehicles
  • Dynamics Modeling, Simulation Analyses, and Control System Design
  • Advanced Driver Assistance Systems
  • Application of AI and Machine Learning for Driver–Vehicle System
  • Human Factors for Intelligent Vehicles
  • X-by-Wire Control and Optimization Design of Intelligent Vehicles

Dr. Zejiang Wang
Dr. Jinhao Liang
Dr. Xingyu Zhou
Guest Editors

Dr. Zhenwu Fang
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous driving
  • intelligent vehicles
  • driver assistance systems
  • machine learning
  • AI

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

14 pages, 479 KiB  
Article
Event-Triggered Cruise Control of Connected Automated Vehicle Platoon Subject to Input Limitations
by Chaobin Zhou, Jian Gong, Qing Ling and Jinhao Liang
Machines 2024, 12(12), 866; https://doi.org/10.3390/machines12120866 - 28 Nov 2024
Viewed by 444
Abstract
This article proposes event-triggered cruise control in platoons of connected automated vehicles (CAVs) with heterogeneous input limitations. A distributed control protocol is developed to ensure the stability and performance of the platoon, explicitly addressing varying levels of input saturation among vehicles. To further [...] Read more.
This article proposes event-triggered cruise control in platoons of connected automated vehicles (CAVs) with heterogeneous input limitations. A distributed control protocol is developed to ensure the stability and performance of the platoon, explicitly addressing varying levels of input saturation among vehicles. To further enhance communication efficiency, a centralized event-triggered mechanism is introduced, activating control updates only when necessary, effectively preventing Zeno behaviors through a predefined threshold. The proposed approach not only achieves global asymptotic stability but also significantly reduces communication demands, making it suitable for real-world driving conditions characterized by input constraints. Simulation results validate the effectiveness and robustness of the proposed control strategy, demonstrating its potential for practical implementation in intelligent transportation systems. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Intelligent Vehicles)
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19 pages, 2951 KiB  
Article
Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity
by Qinghua Cao, Jian Wu, Fuxing Xu, Xinhong Miao, Mingjie Guo and Yuan Chu
Machines 2024, 12(10), 698; https://doi.org/10.3390/machines12100698 - 2 Oct 2024
Viewed by 744
Abstract
Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time [...] Read more.
Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Intelligent Vehicles)
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19 pages, 4909 KiB  
Article
An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems
by Haizhu Xu, Duanyang Geng, Zhixian Fan, Dexi Wu and Meizhou Chen
Machines 2024, 12(9), 663; https://doi.org/10.3390/machines12090663 - 21 Sep 2024
Viewed by 4067
Abstract
The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and [...] Read more.
The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Intelligent Vehicles)
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