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Intelligent Control Systems for Autonomous Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 887

Special Issue Editors

Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA
Interests: model predictive control; reinforcement learning; connected and automated vehicles; electric vehicles; renewable energy systems
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Guest Editor
Department of Electrical and Computer Engineering, Kettering University, Flint, MI 48504, USA
Interests: systems science and control theory with emphasis on vehicle health management using vehicle dynamics theory and machine learning algorithms; resilient control of autonomous vehicles

Special Issue Information

Dear Colleagues,

As autonomous vehicles continue to revolutionize the transportation landscape, this Special Issue aims to showcase innovative research at the intersection of sensor technologies and intelligent control systems. We invite researchers, engineers and experts to contribute their latest findings and methodologies that enhance the intelligence and autonomy of vehicles. Topics of interest include sensor fusion, machine learning algorithms, real-time decision-making and planning, human–machine interactions and robust control strategies. By delving into these crucial aspects, we seek to address the challenges and opportunities in develo** intelligent control solutions that pave the way for safer, more efficient and reliable autonomous transportation.

This Special Issue provides a platform for interdisciplinary discussions and fosters collaboration between researchers in sensor technology and control systems. We anticipate that the collected submissions will significantly contribute to the ongoing dialogue on sha** the future of autonomous vehicles. Submissions are welcome from researchers worldwide who are at the forefront of this transformative field.

Topics include, but not limited to:

  • Autonomous vehicles;
  • Intelligent control;
  • Sensor fusion;
  • Machine learning-based vehicle sensor and control;
  • Real-time decision-making and planning;
  • Robust control;
  • Human–machine interaction;
  • Predictive control;
  • Sensor technologies;
  • Autonomous systems.

Dr. Jun Chen
Dr. Wen-Chiao Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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
  • human–machine interaction
  • sensor fusion

Published Papers (1 paper)

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Research

31 pages, 10072 KiB  
Article
Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method
by **ang Li, Gang Li and Zijian Bian
Sensors 2024, 24(12), 3899; https://doi.org/10.3390/s24123899 - 16 Jun 2024
Viewed by 709
Abstract
For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) [...] Read more.
For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving. Full article
(This article belongs to the Special Issue Intelligent Control Systems for Autonomous Vehicles)
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