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Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport...
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Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1119

Special Issue Editors

Dr. Yujie Shen

E-Mail Website
Guest Editor
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China
Interests: advanced vehicle dynamics simulations and control; vibration control
Special Issues, Collections and Topics in MDPI journals
Dr. Sifeng Bi

E-Mail Website
Guest Editor
Department of Aeronautics and Astronautics, University of Southampton, Southampton, UK
Interests: uncertainty quantification and model updating of transport equipment

Special Issue Information

Dear Colleagues,

Advanced environment perception, decision planning, and intelligent control of transport equipment concentrate on developing technologies that enable transportation systems to operate with unprecedented autonomy, efficiency, and safety. It encompasses sophisticated sensors and algorithms to perceive and interpret complex environmental data in real-time, sophisticated decision-making frameworks to plan optimal routes and actions based on these data, and intelligent control systems to execute these plans precisely and adaptably. By integrating advancements in artificial intelligence, machine learning, and robotics, this field aims to revolutionize how we move goods and people, making transportation more sustainable, reliable, and user-friendly. This Special Issue focuses on advanced environmental sensing, information interaction, decision planning, intelligent driving, and dynamics control methods. Related topics include, but are not limited to:

  • Traffic environment sensing, information interaction, and cooperative control technologies for transport equipment and the road environment under complex traffic conditions.
  • Path planning and intelligent driving methods for transport equipment under full working conditions.
  • Transport equipment dynamics pre-aiming control based on traffic environment sensing, which includes the suspension, steering, braking, chassis, and powertrain systems.
  • Computer-aided modeling, simulation, validation, parameter identification, testing, and driver modeling.
  • New energy transport equipment, intelligent transport equipment, and automated traffic systems are related to traffic and transportation.

Dr. Yujie Shen
Dr. Sifeng Bi
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. 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

  • transport equipment
  • environment perception
  • decision planning
  • intelligent control

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Published Papers (3 papers)

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Research

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20 pages, 2169 KiB  
Article
Lightweight CNN-Based Visual Perception Method for Assessing Local Environment Complexity of Unmanned Surface Vehicle
by Tulin Li, Xiufeng Zhang, Yingbo Huang and Chunxi Yang
Sensors 2025, 25(3), 980; https://doi.org/10.3390/s25030980 - 6 Feb 2025
Viewed by 569
Abstract
Addressing the problem of inadequate environmental detection in the process of optimizing search for unmanned surface vehicles (USVs) by a heuristic algorithm, this paper proposes a comprehensive visual perception method that combines a lightweight convolutional neural network (CNN) with the USV’s real-time heading [...] Read more.
Addressing the problem of inadequate environmental detection in the process of optimizing search for unmanned surface vehicles (USVs) by a heuristic algorithm, this paper proposes a comprehensive visual perception method that combines a lightweight convolutional neural network (CNN) with the USV’s real-time heading angle. This method employs a multi-feature input CNN with residual learning blocks, which takes both the current local environmental images and heading angle features as inputs to identify the complexity of the local environment with higher accuracy and a smaller load size. Meanwhile, human expertise is incorporated to classify labels through a majority voting system, thereby making the model’s perceptual classification more intuitive and allowing it to possess a human-like comprehensive perception ability compared to systems with classification methods with several parameters. Subsequently, this identification result can be used as feedback for the heuristic algorithm to optimize and plan the USV’s path. The simulation results indicate that the developed model achieves an 80% reduction in model size while maintaining an accuracy exceeding 90%. The proposed method significantly improves the environment recognition capability of the heuristic algorithm, enhances optimization search efficiency, and increases the overall performance of path planning by approximately 21%. Full article
(This article belongs to the Special Issue Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment)
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Review

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25 pages, 1405 KiB  
Review
A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving
by Hai Wang, Junhao Liu, Haoran Dong and Zheng Shao
Sensors 2025, 25(9), 2794; https://doi.org/10.3390/s25092794 - 29 Apr 2025
Viewed by 63
Abstract
Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in [...] Read more.
Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion’s development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields. Full article
(This article belongs to the Special Issue Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment)
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36 pages, 1802 KiB  
Review
A Review of Vision-Based Multi-Task Perception Research Methods for Autonomous Vehicles
by Hai Wang, Jiayi Li and Haoran Dong
Sensors 2025, 25(8), 2611; https://doi.org/10.3390/s25082611 - 20 Apr 2025
Viewed by 193
Abstract
Multi-task perception technology for autonomous driving significantly improves the ability of autonomous vehicles to understand complex traffic environments by integrating multiple perception tasks, such as traffic object detection, drivable area segmentation, and lane detection. The collaborative processing of these tasks not only improves [...] Read more.
Multi-task perception technology for autonomous driving significantly improves the ability of autonomous vehicles to understand complex traffic environments by integrating multiple perception tasks, such as traffic object detection, drivable area segmentation, and lane detection. The collaborative processing of these tasks not only improves the overall performance of the perception system but also enhances the robustness and real-time performance of the system. In this paper, we review the research progress in the field of vision-based multi-task perception for autonomous driving and introduce the methods of traffic object detection, drivable area segmentation, and lane detection in detail. Moreover, we discuss the definition, role, and classification of multi-task learning. In addition, we analyze the design of classical network architectures and loss functions for multi-task perception, introduce commonly used datasets and evaluation metrics, and discuss the current challenges and development prospects of multi-task perception. By analyzing these contents, this paper aims to provide a comprehensive reference framework for researchers in the field of autonomous driving and encourage more research work on multi-task perception for autonomous driving. Full article
(This article belongs to the Special Issue Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment)
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