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Advancements and Applications of Cooperative Positioning, Planning and Control for Autonomous Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1425

Special Issue Editor

Dr. Chaoyang Jiang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: localization and control for connected vehicles; SLAM; localization for unmanned systems; information fusion for smart city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the Sensors journal, titled “Advancements and Applications of Cooperative Localization, Planning and Control for Autonomous Vehicles”, aims to explore the latest developments and practical applications of collaborative techniques in the field of vehicle positioning, planning and control. Cooperative localization refers to the integration of sensing information from multiple vehicles to improve the accuracy and reliability of vehicle positioning in various environments. Cooperative planning involves coordinating the actions and trajectories of multiple vehicles to more effectively and efficiently achieve a common objective such as task allocation, trajectory planning and collision avoidance. Cooperative control for multiple vehicles involves designing control strategies that enable a group of vehicles to work together towards a common objective such as platooning and formation control, while taking into account the dynamics and constraints of each individual vehicle.

With the rapid advancement of communication technology, cooperative localization, planning and control have received significant attention due to their potential to enhance autonomous driving capabilities, safety and overall transportation efficiency. This Special Issue invites researchers, engineers, and practitioners to contribute original research articles, reviews, and perspectives on the advancements and applications of cooperative localization, planning and control for autonomous vehicles.

Topics of interest include, but are not limited to, the following:

  • Sensor fusion for vehicle localization;
  • Real-time data fusion and sensor calibration techniques;
  • Simultaneous localization and mapping;
  • Seamless localization ;
  • Vehicle localization in challenging environments (e.g., urban canyons, tunnels, and dense foliage);
  • Cooperative localization and mapping;
  • Cooperative localization for homogeneous or heterogeneous connected vehicles;
  • Formation system of unmanned connected vehicles;
  • Cooperative planning and decision-making for connected vehicles;
  • Task allocation for multiple unmanned vehicles;
  • Cooperative control for connected vehicles;
  • Wireless communication and networking for connected vehicles.

Dr. Chaoyang Jiang
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. 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

  • connected vehicles
  • cooperative localization
  • cooperative planning
  • cooperative control
  • multi-sensor fusion
  • SLAM
  • trajectory planning
  • formation control

Published Papers (3 papers)

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Research

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21 pages, 10239 KiB  
Article
A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction
by Hao Huang, Yongbiao Hu and Xuebin Wang
Sensors 2024, 24(13), 4408; https://doi.org/10.3390/s24134408 - 7 Jul 2024
Viewed by 537
Abstract
As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion [...] Read more.
As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels. Full article
(This article belongs to the Special Issue Advancements and Applications of Cooperative Positioning, Planning and Control for Autonomous Vehicles)
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20 pages, 24883 KiB  
Article
Single-Line LiDAR Localization via Contribution Sampling and Map Update Technology
by Xiaoxu Jiang, David K. Yang, Zhenyu Tian, Gang Liu and Mingquan Lu
Sensors 2024, 24(12), 3927; https://doi.org/10.3390/s24123927 - 17 Jun 2024
Viewed by 367
Abstract
Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its [...] Read more.
Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments. Full article
(This article belongs to the Special Issue Advancements and Applications of Cooperative Positioning, Planning and Control for Autonomous Vehicles)
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Review

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34 pages, 1761 KiB  
Review
A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations
by Taokai Xia and Hui Chen
Sensors 2024, 24(15), 4808; https://doi.org/10.3390/s24154808 - 24 Jul 2024
Viewed by 277
Abstract
Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory [...] Read more.
Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed. Full article
(This article belongs to the Special Issue Advancements and Applications of Cooperative Positioning, Planning and Control for Autonomous Vehicles)
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