Decision Making, Planning and Control of Autonomous Vehicles

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2133

Editor


E-Mail Website
Guest Editor
Department of Mechanical Engineering, Alexandria University, Alexandria, Egypt
Interests: control of nonlinear systems; fuzzy logic and artificial neural networks; vehicle dynamics; mechanical vibrations; robot kinematics and robot singularity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles are highly dependent on the interconnectedness of decision, plan and control tasks to run efficiently in a safe environment when faced with complicated, unpredictable, dynamic scenarios.  Although significant progress has been made, the challenge of robustness still remains a problem because of unclear perceptions, unpredictable behavior from agents, real-time constraints and safety factors. This Special Issue proposes to detail breakthroughs that can overcome such challenges with novel theories, models and system design paradigms.

This Special Issue is centered on frameworks for decision making under uncertainty, risk-aware and ethically sound autonomy, as well as learning-assisted approaches to behavior synthesis and policy optimization.

Techniques for motion planning and trajectory synthesis that seek to optimize, keep safe and compute efficiently are of particular interest, as are control solutions that ensure robust stability, flexibility and soundness in most operating conditions. Solutions combining control with learning, provided they address safety, interpretability and soundness, are especially welcome.

This Special Issue also calls for contributions on cooperative and networked autonomous systems, multi-agent interactions as well as human–vehicle coexistence in shared environments. The Special Issue proposes to facilitate the development of trustworthy, scalable, robust and reliable autonomous vehicle technologies for future intelligent transportation systems and autonomous robotic systems by exploring real-world requirements in relation to theoretical foundations.

Prof. Dr. Ossama Mokhiamar
Guest Editor

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-anonymized 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
  • decision making under uncertainty
  • motion and path planning
  • trajectory optimization
  • autonomous control systems
  • safety and verification
  • human–vehicle interaction
  • intelligent transportation systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 8039 KB  
Article
Occupant-Aware Decision-Making with Large Vision-Language Model for Autonomous Vehicles
by Titong Jiang, Xinyu Zhao, Xuewu Ji and Yahui Liu
Machines 2026, 14(3), 257; https://doi.org/10.3390/machines14030257 - 25 Feb 2026
Viewed by 1666
Abstract
Autonomous driving (AD) has emerged as a transformative technology that holds the potential to free humans from the need for manual driving and provide a safer, more comfortable and efficient driving experience. However, most AD systems make decisions solely based on vehicle dynamics [...] Read more.
Autonomous driving (AD) has emerged as a transformative technology that holds the potential to free humans from the need for manual driving and provide a safer, more comfortable and efficient driving experience. However, most AD systems make decisions solely based on vehicle dynamics and environmental factors such as road conditions and surrounding vehicles, while the occupant’s mental states, such as subjective feelings and experience, are neglected. As a result, autonomous vehicles (AVs) often fail to meet the occupant’s physical and mental demands, ultimately leading to a compromised driving experience. In this study, we propose an occupant-aware decision-making paradigm (ODP) for AD systems. ODP first perceives the occupant’s physical and physiological states that are closely related to mental states, such as facial expressions and physiological signals, through the occupant monitoring system (OMS). Then, a large vision-language model (VLM) processes the occupant’s physical and physiological states via the chain of thought (CoT) technique to analyze the occupant’s mental states and infer the occupant’s needs. Finally, the VLM makes driving decisions that match the occupant’s demands and preferences. Experimental results show that ODP can make decisions that are significantly better aligned with the occupant’s actual needs than existing methods. Full article
(This article belongs to the Special Issue Decision Making, Planning and Control of Autonomous Vehicles)
Show Figures

Figure 1

Back to TopTop