Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 846

Special Issue Editor

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: motion planning; computational optimal control; numerical optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An autonomous vehicle refers operates without a human driver. There has been rapid progress made in the applications of autonomous vehicles in both structured urban road environments in unstructured indoor scenarios. Planning and control are two critical modules in an autonomous vehicle system. Concretely, the planning module is responsible for generating an open-loop trajectory, while the control module can track the desired reference trajectory from the planning module in a closed-loop fashion and under all possible road, weather, and driving conditions, including abnormal conditions such as physical failures and cyberattacks. Planning and control modules are important as they directly reflect the intelligence level of an autonomous system.

The purpose of this Special Issue is to present the most recent advances in planning or control methodologies used for autonomous vehicles. Submitted papers should focus on how the proposed planning and/or control method can solve real-world problems. The editorial board will maintain a high standard in order to prescreen submissions that simply propose a generic method without sufficient discussions of its potential to address the real-world bottleneck problems in the realm of autonomous driving. Note that we also welcome papers that discuss methods relevant to planning or control, provided they are able to improve the planning or control module performance.

Topics of interest include but are not limited to:

  • Path/trajectory/motion planning and replanning;
  • Path/trajectory/motion control;
  • On-road/off-road planning and control;
  • Modeling and simulation method for planning and/or control;
  • Testing and validation methods related to planning and/or control;
  • Safety-related issues with planning and control;
  • Security-related issues with planning and control;
  • Human-machine interaction related to planning and/or control;
  • Intelligent techniques/methods to plan and/or control;
  • Integration of planning and control;
  • Reviews of planning or control methodologies;
  • Data-driven/model-based planning or control;
  • Comparisons among different types of planning or control methods;
  • Fault-tolerant planning and control;
  • Cooperative planning and control;
  • Real-world applications of planning and control.

Prof. Dr. Bai Li
Guest Editor

Manuscript Submission Information

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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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • motion planning
  • path planning
  • trajectory planning
  • motion control
  • path tracking
  • trajectory tracking
  • autonomous driving
  • unmanned system

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Published Papers (1 paper)

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Research

18 pages, 5649 KiB  
Article
A Data-Driven Path-Tracking Model Based on Visual Perception Behavior Analysis and ANFIS Method
by Ziniu Hu, Yue Yu, Zeyu Yang, Haotian Zhu, Lvfan Liu and Yunshui Zhou
Electronics 2024, 13(1), 61; https://doi.org/10.3390/electronics13010061 - 21 Dec 2023
Viewed by 575
Abstract
This paper proposes a data-driven human-like driver model (HDM) based on the analysis and understanding of human drivers’ behavior in path-tracking tasks. The proposed model contains a visual perception module and a decision-making module. The visual perception module was established to extract the [...] Read more.
This paper proposes a data-driven human-like driver model (HDM) based on the analysis and understanding of human drivers’ behavior in path-tracking tasks. The proposed model contains a visual perception module and a decision-making module. The visual perception module was established to extract the visual inputs, including road information and vehicle motion states, which can be perceived by human drivers. The extracted inputs utilized for lateral steering decisions can reflect specific driving skills exhibited by human drivers like compensation control, preview behavior, and anticipation ability. On this basis, an adaptive neuro-fuzzy inference system (ANFIS) was adopted to design the decision-making module. The inputs of the ANFIS include the vehicle speed, lateral deviation in the near zone, and heading angle error in the far zone. The output is the steering wheel angle. ANFIS can mimic the fuzzy reasoning characteristics of human driving behavior. Next, a large amount of human driving data was collected through driving simulator experiments. Based on the data, the HDM was established. Finally, the results of the joint simulation under PreScan/MATLAB verified the superior performances of the proposed HDM. Full article
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