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Electronics
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Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control
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Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 5060

Special Issue Editors

Dr. Ying Zhang

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710060, China
Interests: system cognition; human–machine cooperation; swarm intelligence; autonomous decision-making; energy-saving control with application to electric; connected and autonomous vehicles (e-CAVs) and unmanned aerial vehicles (UAVs)
Dr. Hao Chen

E-Mail Website
Guest Editor
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: electric machines; power electronic drives; motor control
Dr. Jinchao Chen

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710060, China
Interests: simulation; verification; decision-making and intelligent control of unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the ever-accelerated development and progressive maturation of significant technologies, autonomous vehicles (e.g., self-driving cars, unmanned aerial vehicles, etc.) have moved from the stage of laboratories to open road field tests and even commercial demonstrations in urban driving scenarios and specific traffic environments. Autonomous vehicles play an increasingly important role in the national economy and human social life in many fields. Compared with human-driven vehicles, autonomous vehicles possess many advanced functions that do not require driver involvement, such as lane changing/keeping, adaptive cruise control, decision-making, and motion control. Due to the dynamic and uncertain nature of driving scenarios, specific traffic environments impose many theoretical and technical challenges on existing autonomous vehicle techniques. For these reasons, how autonomous vehicles can be programmed to behave in different driving situations to guarantee safety and efficiency remain among the crucial knowledge gaps that require scientific research.

This Special Issue calls for papers presenting novel works regarding scene understanding, trajectory prediction, decision-making, motion planning and the intelligent control of autonomous vehicles. Manuscripts considered for possible publication may focus on, but are not limited to, the following areas:

  • The environmental perception of autonomous vehicles in specific driving scenarios;
  • The modeling of autonomous vehicles in specific driving scenarios;
  • Decision-making for the improved safety of autonomous vehicles;
  • Multi-agent reinforcement learning for autonomous vehicles;
  • The trajectory prediction of autonomous vehicles in specific driving scenarios;
  • Localization, mapping and the connection of autonomous vehicles;
  • The motion planning of autonomous vehicles;
  • Eco-driving in autonomous vehicles;
  • Human–machine collaborative control of autonomous vehicles;
  • Vehicle motion control in complex traffic environments;
  • Vehicle-to-infrastructure cooperation.

Dr. Ying Zhang
Dr. Hao Chen
Dr. Jinchao Chen
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 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

  • autonomous vehicles
  • trajectory prediction
  • motion planning
  • intelligent driving
  • vehicle motion control

Published Papers (5 papers)

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Research

16 pages, 849 KiB  
Article
Self-Evaluation of Trajectory Predictors for Autonomous Driving
by Phillip Karle, Lukas Furtner and Markus Lienkamp
Electronics 2024, 13(5), 946; https://doi.org/10.3390/electronics13050946 - 29 Feb 2024
Cited by 1 | Viewed by 615
Abstract
Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively [...] Read more.
Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively researched with several competing large datasets, and established methods provide promising results. However, the incorporation of scenario understanding has been sparsely investigated. It comprises two aspects. First, by means of scenario understanding, individual assumptions of an object’s behavior can be derived to adaptively predict its future motion. Second, scenario understanding enables the detection of challenging scenarios for autonomous vehicle software to prevent safety-critical situations. Therefore, we propose a method incorporating scenario understanding into the motion prediction task to improve adaptivity and avoid prediction failures. This is realized by an a priori evaluation of the scenario based on semantic information. The evaluation adaptively selects the most accurate prediction model but also recognizes if no model is capable of accurately predicting this scenario and high prediction errors are expected. The results on the comprehensive scenario library CommonRoad reveal a decrease in the Euclidean prediction error by 81.0% and a 90.8% reduction in mispredictions of our method compared to the benchmark model. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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29 pages, 5823 KiB  
Article
A Personalized Motion Planning Method with Driver Characteristics in Longitudinal and Lateral Directions
by Di Zeng, Ling Zheng, Yinong Li, Jie Zeng and Kan Wang
Electronics 2023, 12(24), 5021; https://doi.org/10.3390/electronics12245021 - 15 Dec 2023
Viewed by 687
Abstract
Humanlike driving is significant in improving the safety and comfort of automated vehicles. This paper proposes a personalized motion planning method with driver characteristics in longitudinal and lateral directions for highway automated driving. The motion planning is decoupled into path optimization and speed [...] Read more.
Humanlike driving is significant in improving the safety and comfort of automated vehicles. This paper proposes a personalized motion planning method with driver characteristics in longitudinal and lateral directions for highway automated driving. The motion planning is decoupled into path optimization and speed optimization under the framework of the Baidu Apollo EM motion planner. For modeling driver behavior in the longitudinal direction, a car-following model is developed and integrated into the speed optimizer based on a weight ratio hypothesis model of the objective functional, whose parameters are obtained by Bayesian optimization and leave-one-out cross validation using the driving data. For modeling driver behavior in the lateral direction, a Bayesian network (BN), which maps the physical states of the ego vehicle and surrounding vehicles and the lateral intentions of the surrounding vehicles to the driver’s lateral intentions, is built in an efficient and lightweight way using driving data. Further, a personalized reference trajectory decider is developed based on the BN, considering traffic regulations, the driver’s preference, and the costs of the trajectories. According to the actual traffic scenarios in the driving data, a simulation is constructed, and the results validate the human likeness of the proposed motion planning method. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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27 pages, 36598 KiB  
Article
SoC-VRP: A Deep-Reinforcement-Learning-Based Vehicle Route Planning Mechanism for Service-Oriented Cooperative ITS
by Boyuan Hou, Kailong Zhang, Zu Gong, Qiugang Li, Junle Zhou, Jiahao Zhang and Arnaud de La Fortelle
Electronics 2023, 12(20), 4191; https://doi.org/10.3390/electronics12204191 - 10 Oct 2023
Cited by 4 | Viewed by 1319
Abstract
With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the [...] Read more.
With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the Vehicle Routing Problem (VRP) is a significant concern addressed in this work. Considering the varying urgency levels of different vehicles and their different traveling constraints in the Service-oriented Cooperative ITS (SoC-ITS) framework studied in our previous research, the Service-oriented Cooperative Vehicle Routing Problem (SoC-VRP) is firstly analyzed, in which cooperative planning and vehicle urgency degrees are two vital factors. After examining the characteristics of both VRP and SoC-VRP, a Deep Reinforcement Learning (DRL)-based prioritized route planning mechanism is proposed. Specifically, we establish a deep reinforcement learning model with Rainbow DQN and devise a prioritized successive decision-making route planning method for SoC-ITS, where vehicle urgency degrees are mapped to three priorities: High for emergency vehicles, Medium for shuttle buses, and Low for the rest. All proposed models and methods are implemented, trained using various scenarios on typical road networks, and verified with SUMO-based scenes. Experimental results demonstrate the effectiveness of this hybrid prioritized route planning mechanism. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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21 pages, 5262 KiB  
Article
Research on Multiple Air-To-Air Refueling Planning Based on Multi-Dimensional Improved NSGA-II Algorithm
by Zhihao Zhang, Zhouhang Huang, Xiaodong Liu and Boyu Feng
Electronics 2023, 12(18), 3880; https://doi.org/10.3390/electronics12183880 - 14 Sep 2023
Viewed by 860
Abstract
Reasonable air-to-air refueling planning (AARP) is essential for the successful completion of remote flight missions. A comprehensive task model for air refueling planning is proposed, and the key constraints are determined. The multi-objective optimization algorithm NSGA-II is improved from three distinct perspectives. The [...] Read more.
Reasonable air-to-air refueling planning (AARP) is essential for the successful completion of remote flight missions. A comprehensive task model for air refueling planning is proposed, and the key constraints are determined. The multi-objective optimization algorithm NSGA-II is improved from three distinct perspectives. The performance of the improved NSGA-II was evaluated by selecting test functions from the ZDT series for comparison against the original version. Simulation experiments demonstrate that the improved NSGA-II yields an increase in the average hypervolume index by approximately 10% to 18%, a decrease in the average spacing index by about 22% to 57%, and a reduction in the standard deviation of hypervolume by 27% to 76%. The obtained findings demonstrate that the improved NSGA-II variant exhibits superior convergence, uniformity, and universality. The airspace of the Americas was selected as the mission area to generate 50 AARP schemes for application. Five representative schemes with fuel consumption from 47,083 kg to 104,735 kg, corresponding to time consumption coefficient from 1.27 to 1.07, were chosen as alternatives. This research can enhance the efficiency and stability of air-to-air refueling planning, thereby serving as a valuable theoretical reference for selecting appropriate remote multi-point air refueling schemes. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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19 pages, 3595 KiB  
Article
Application of the Relative Orbit in an On-Orbit Service Mission
by Xuehua Li, Lei Zhang, Zhijun Li and Xingsuo He
Electronics 2023, 12(14), 3034; https://doi.org/10.3390/electronics12143034 - 11 Jul 2023
Viewed by 723
Abstract
To achieve an on-orbit service mission, the mission spacecraft must approach the target spacecraft first, which is based on the spacecraft’s relative motion. To enhance the safety and reliability of on-orbit service missions, the relative hovering orbit was proposed and needed to be [...] Read more.
To achieve an on-orbit service mission, the mission spacecraft must approach the target spacecraft first, which is based on the spacecraft’s relative motion. To enhance the safety and reliability of on-orbit service missions, the relative hovering orbit was proposed and needed to be studied further. A high-precision design method for hovering orbit is presented based on the relative dynamics model of spacecraft in this paper. Firstly, based on the stability analysis of the spacecraft relative dynamics model, a method to determine the initial value of periodic relative motion orbit is explored, and an example is given to verify the validity of the method. Then, through theoretical analysis, the formulae of control acceleration required during the hovering flying mission were put forward for both without considering perturbation and with considering J2 perturbation, and numerical simulations for hovering orbit were made to verify the feasibility of the approaches proposed. Simulation results show that the control acceleration curves are smooth, which indicates that the hovering flying mission is easier to achieve, and the control method based on sliding mode control theory is adopted for hovering control. The relative hovering method proposed can provide references in space on-orbit service missions for practical engineers. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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