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Recent Advances in Motion Planning and Control of Autonomous Vehicles,...
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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 February 2025 | Viewed by 5455

Special Issue Editor

Prof. Dr. Bai Li

E-Mail Website1 Website2
Guest 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

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. Electronics 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 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

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

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Related Special Issue

Published Papers (4 papers)

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Research

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20 pages, 9544 KiB  
Article
Research on a Personalized Decision Control Algorithm for Autonomous Vehicles Based on the Reinforcement Learning from Human Feedback Strategy
by Ning Li and Pengzhan Chen
Electronics 2024, 13(11), 2054; https://doi.org/10.3390/electronics13112054 - 24 May 2024
Viewed by 722
Abstract
To address the shortcomings of previous autonomous decision models, which often overlook the personalized features of users, this paper proposes a personalized decision control algorithm for autonomous vehicles based on RLHF (reinforcement learning from human feedback). The algorithm combines two reinforcement learning approaches, [...] Read more.
To address the shortcomings of previous autonomous decision models, which often overlook the personalized features of users, this paper proposes a personalized decision control algorithm for autonomous vehicles based on RLHF (reinforcement learning from human feedback). The algorithm combines two reinforcement learning approaches, DDPG (Deep Deterministic Policy Gradient) and PPO (proximal policy optimization), and divides the control scheme into three phases including pre-training, human evaluation, and parameter optimization. During the pre-training phase, an agent is trained using the DDPG algorithm. In the human evaluation phase, different trajectories generated by the DDPG-trained agent are scored by individuals with different styles, and the respective reward models are trained based on the trajectories. In the parameter optimization phase, the network parameters are updated using the PPO algorithm and the reward values given by the reward model to achieve personalized autonomous vehicle control. To validate the control algorithm designed in this paper, a simulation scenario was built using CARLA_0.9.13 software. The results demonstrate that the proposed algorithm can provide personalized decision control solutions for different styles of people, satisfying human needs while ensuring safety. Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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26 pages, 18317 KiB  
Article
Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision
by Zhaobo Qin, Mulin Han, Zhe Xing, Hongmao Qin, Ming Gao and Manjiang Hu
Electronics 2024, 13(9), 1760; https://doi.org/10.3390/electronics13091760 - 2 May 2024
Cited by 1 | Viewed by 1060
Abstract
Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision [...] Read more.
Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety. Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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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 860
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
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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Review

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45 pages, 17267 KiB  
Review
Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions
by Tantan Zhang, Haipeng Liu, Weijie Wang and Xinwei Wang
Electronics 2024, 13(17), 3486; https://doi.org/10.3390/electronics13173486 - 2 Sep 2024
Viewed by 2001
Abstract
Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios [...] Read more.
Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation. Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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