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Machines
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Intelligent Sensing, Planning and Control for Autonomous Ground Vehicles
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Intelligent Sensing, Planning and Control for Autonomous Ground Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 694

Special Issue Editors

Dr. Xianjian Jin

E-Mail Website
Guest Editor
Intelligent Electric Vehicle Laboratory (iEVL), Department of Mechanical Engineering, Shanghai University, Shanghai, China
Interests: vehicle system dynamics and control; advanced electric vehicles; automated driving; intelligent and connected vehicle
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Xia

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
Interests: automated driving; intelligent and connected vehicle

Special Issue Information

Dear Colleagues,

Autonomous ground vehicles (AGVs), especially fully autonomous ones, are expected to significantly provide increased highway capacity and traffic mobility. This encompasses faster response times, less fuel consumption and environmental pollution, along with foresighted driving, greater driving safety benefits and convenience with intelligent driving by applying vehicle-to-other entities (V2X). As an important part of future intelligent transportation systems, autonomous ground vehicles have attracted attention from academia, industry and governments due to their potential applications, such as automated highways and  urban  transportation. However, AGVs with automated driving technologies will lead to complex challenges, including the following: high-precision environment perception schemes using low-cost sensors and information fusion extended from multi-source sensors; human-like decision making and motion planning of automated driving for surrounding dynamic traffic objects such as other vehicles, cyclists, and pedestrians; advanced chassis dynamics modeling and control technologies in the presence of vehicles’ active safety system nonlinearities and parameter uncertainties; hybrid powertrain configurations and energy optimization and saving strategies for maximizing the true potential of connected vehicles; the design of new V2X communication protocols; and the creation for in-vehicle network applications against vehicle network bandwidth limitations. The main objective of this Special Issue is to provide an opportunity for scientists, engineers, and practitioners to exhibit novel theoretical and technological breakthroughs in autonomous ground vehicles.

Dr. Xianjian Jin
Dr. Xin Xia
Guest Editors

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 ground vehicles
  • automated driving
  • automated highways
  • urban transportation
  • connected vehicles

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Published Papers (2 papers)

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Research

24 pages, 4703 KiB  
Article
Deep Reinforcement Learning-Based Active Disturbance Rejection Control for Trajectory Tracking of Autonomous Ground Electric Vehicles
by Xianjian Jin, Huaizhen Lv, Yinchen Tao, Jianning Lu, Jianbo Lv and Nonsly Valerienne Opinat Ikiela
Machines 2025, 13(6), 523; https://doi.org/10.3390/machines13060523 - 16 Jun 2025
Viewed by 144
Abstract
This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with [...] Read more.
This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. More specifically, it combines the Line of Sight (LOS) guidance rate with ADRC, proves the stability of LOS through the Lyapunov law, and designs a yaw angle controller, using the extended state observer to reduce the impact of disturbances on tracking accuracy. And the approach also addresses the nonlinear vehicle dynamic characteristics of AGEVs while mitigating internal and external disturbances by leveraging the inherent decoupling capability of ADRC and the data-driven parameter adaptation capability of DDPG. Simulations via CarSim/Simulink are carried out to validate the controller performance in serpentine and double-lane-change maneuvers. The simulation results show that the proposed framework outperforms traditional control strategies with significant improvements in lateral tracking accuracy, yaw stability, and sideslip angle suppression. Full article
(This article belongs to the Special Issue Intelligent Sensing, Planning and Control for Autonomous Ground Vehicles)
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22 pages, 329 KiB  
Article
Comprehensive MILP Formulation and Solution for Simultaneous Scheduling of Machines and AGVs in a Partitioned Flexible Manufacturing System
by Cheng Zhuang, Jingbo Qu, Tianyu Wang, Liyong Lin, Youyi Bi and Mian Li
Machines 2025, 13(6), 519; https://doi.org/10.3390/machines13060519 - 13 Jun 2025
Viewed by 266
Abstract
This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while [...] Read more.
This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. Three different workshop layouts are analyzed, with varying numbers of machines in partitioned workshop areas A and B, to evaluate the performance and effectiveness of the proposed model. The model is tested in multiple scenarios that combine different layouts with varying numbers of workpieces, followed by an extension to consider dynamic initial conditions in a more generalized MILP framework. Results demonstrate that the proposed MILP formulation efficiently generates globally optimal solutions and consistently outperforms a greedy algorithm enhanced by A*-inspired heuristics. Although computationally intensive for large scenarios, the MILP’s optimal results serve as an exact benchmark for evaluating faster heuristic methods. In addition, the study provides practical insight into the integration of AGVs in modern manufacturing systems, paving the way for more flexible and efficient production planning. The findings of this research are expected to contribute to the development of advanced scheduling strategies in automated manufacturing systems. Full article
(This article belongs to the Special Issue Intelligent Sensing, Planning and Control for Autonomous Ground Vehicles)
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