Advances in Autonomous Vehicles Dynamics and Control, 2nd Edition

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 325

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Automation, Northeastern, Shenyang 110819, China
Interests: intelligent vehicle; mobile robots
Special Issues, Collections and Topics in MDPI journals
Department of Electromechanical Engineering, University of Macau, Macau, China
Interests: intelligent control; dynamics and control; mechanism and machine theory; autonomous system; fault tolerant control; artificial intelligence with engineering applications; machine learning methods; signal processing; intelligent transportation; system modeling and identification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: distributed control; robotic path planning; multi-agent systems; distributed learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles present a promising solution to many of the challenges facing the transportation industry, including reducing the number of accidents caused by human error, improving traffic flow, and increasing fuel efficiency. However, to fully reach this technology’s potential, significant technical challenges must be overcome. One of the key challenges is the development of robust and reliable control algorithms that can ensure the safe and smooth operation of autonomous vehicles in a wide range of driving scenarios, including complex urban and highway environments. Furthermore, highly complex nonlinear vehicle dynamics bring significant difficulties to development of control systems.

This Special Issue seeks to gather the latest research and developments in the field of autonomous vehicle dynamics and control. We invite submissions that address a broad range of topics related to this field, including advanced control system designs, dynamic modeling and simulation, and machine learning approaches. Specifically, topics of interest include but are not limited to the following:

  • Dynamic modeling and real-world implementation of autonomous vehicle control systems;
  • Energy-efficient control and optimization for autonomous vehicles;
  • Advanced control system designs for precise control and maneuvering of autonomous vehicles in complex driving scenarios;
  • Data-driven approaches to vehicle dynamic modeling and simulation;
  • Cooperative and coordinated control of multiple autonomous vehicles;
  • Applications of autonomous vehicles in transportation, warehouse, construction, manufacturing, space exploration, etc.

Dr. Zhongchao Liang
Dr. Jing Zhao
Dr. Zhongguo Li
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 vehicles
  • advanced nonlinear control
  • dynamic modeling
  • machine learning

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Published Papers (1 paper)

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Research

22 pages, 9880 KiB  
Article
Dynamic Correction of Preview Weighting in the Driver Model Inspired by Human Brain Memory Mechanisms
by Chang Li, Hengyu Wang, Bo Yang, Haotian Luo, Jianjin Liu and Wei Zheng
Machines 2025, 13(7), 617; https://doi.org/10.3390/machines13070617 - 17 Jul 2025
Viewed by 211
Abstract
Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and [...] Read more.
Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control, 2nd Edition)
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