Artificial Intelligence-Enabled Vehicle Systems: Modeling, Control Optimization and Fault Diagnosis
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 660
Special Issue Editors
Interests: maglev vehicle dynamics; nonlinear control and intelligent monitoring; under-actuated robot; electromechanical system control
Special Issues, Collections and Topics in MDPI journals
Interests: rail transit; operation control; safety and reliability; test and validation
Interests: nonlinear control; mechanical engineering; intelligent vehicle; marine engineering equipment
Special Issue Information
Dear Colleagues,
Advances in artificial intelligence (AI) have opened new frontiers in the design, optimization, and monitoring of intelligent vehicle systems. Recent developments have demonstrated AI’s remarkable potential to enhance vehicle dynamics modeling, optimize control strategies, and improve fault detection and diagnosis, significantly boosting the performance and reliability of transportation systems.
This Special Issue invites original research articles, review papers, and case studies focusing on cutting-edge applications of AI in vehicle system modeling, control optimization, and fault diagnosis. We encourage contributions addressing theoretical advancements, algorithm development, innovative simulation approaches, and practical implementations across various vehicle domains, including autonomous ground vehicles, unmanned aerial vehicles, maglev trains, rail transit, marine vehicles, and related intelligent transportation platforms.
Potential topics include but are not limited to the following:
- AI-based vehicle dynamics modeling and nonlinear system analysis;
- Intelligent and autonomous vehicle control strategies;
- Machine learning techniques for fault detection, isolation, and system health monitoring;
- Reinforcement learning for optimal control and decision-making;
- Digital twins and their applications in intelligent vehicle systems;
- Predictive maintenance and intelligent operation management;
- Sensor fusion and advanced data analytics for vehicle systems;
- AI-driven safety validation and system reliability;
- Human–machine interactions in autonomous vehicle environments;
- Edge computing and distributed AI solutions for real-time control.
We look forward to receiving your valuable contributions to help drive progress in this exciting interdisciplinary research field.
Dr. Yougang Sun
Dr. Hongliang Pan
Dr. Haiyan Qiang
Prof. Dr. Daofang Chang
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 250 words) can be sent to the Editorial Office for assessment.
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
- artificial intelligence
- vehicle dynamics modeling
- intelligent control
- fault diagnosis
- reinforcement learning
- predictive maintenance
- rail transit systems
- digital twin
- intelligent vehicles
- system optimization
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