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Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent...
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Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 25 December 2024 | Viewed by 3278

Special Issue Editors

Dr. Jian Chen

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: bio-inspired perception and embodied intelligent control of unmanned systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qingchun Feng

E-Mail Website
Guest Editor
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
Interests: agricultural robotics control; compliant operation; intelligence sensing technology; deep reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to announce a Special Issue entitled "Trends in Embodied Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems". This Special Issue will complete a group of international papers published in the field of vehicle technology and intelligent transport systems, with its rapid growth in popularity anticipated.

Currently, new embodied intelligence unmanned vehicle technology and intelligent transport systems are in an era of transformation. In the foreseeable future, unmanned vehicles represented by UGV (unmanned ground vehicles) and UAV (unmanned aerial vehicles) will be built using novel ground and air transportation, logistics, and operation systems; these will offer vast potential in various applicative fields of industry and agriculture. The integration of autonomous vehicle technology and aircraft technology is also breeding a new vehicle, namely the flying car, which is also known as a heavy-load vertical-takeoff and landing aircraft. Interactive perception, decision-making with a capacity for learning, and self-growth behavior are important features of embodied intelligence vehicles and intelligent transport systems, such as unmanned driving vehicles, intelligent agricultural machinery equipment, etc. Correspondingly, multi-sensor (LiDAR, millimeter wave radar, and optical sensors) and multi-source information fusion technology, SLAM technology, and bio-inspired visual technology are applied at the perception stage. Brain-inspired intelligence and end-to-end deep learning neural networks are applied to the decision-making stage. Disturbance self-rejection control, integrated control technology, bio-inspired formation control, and manned/unmanned hybrid cooperative control are applied to the behavior control stage.

We welcome manuscripts from all areas of vehicle technology and intelligent transport systems that may be of interest to international readers. To improve the quality and visibility of the journal, we encourage the submission of well-designed studies and high-quality datasets. Original research articles and comprehensive review papers are also welcome. The papers in this Special Issue will be published with full open access after peer review, for the benefit of both authors and readers.

Potential topics include, but are not limited to, the following:

  • Autonomous driving, intelligent driving, and unmanned driving;
  • Embodied intelligence;
  • Perception, cognition, and behavior;
  • SLAM(simultaneous localization and mapping);
  • LiDAR(light detection and ranging), millimeter wave radar, RGB and RGB-D cameras, and multi-spectral optical sensors;
  • Interactive perception;
  • Decision making with learning ability;
  • Self-growth control;
  • Bio-inspired visual perception;
  • Multi-sensor and multi-source information fusion;
  • Brain-inspired intelligence, and end-to-end deep learning neural network;
  • Disturbance observer, and disturbance self-rejection control;
  • Integration technology of perception, decision-making, and control;
  • Bio-inspired formation control;
  • Hybrid cooperative control of manned and unmanned vehicles.

We look forward to your contributions.

Dr. Jian Chen
Dr. Qingchun Feng
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. Drones is an international peer-reviewed open access monthly 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

  • vehicle
  • unmanned systems
  • embodied intelligence
  • agricultural and industrial applications
  • intelligent transport
  • autonomous driving
  • UGV
  • UAV
  • SLAM
  • perception
  • decision-making
  • control

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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21 pages, 716 KiB  
Article
FedBeam: Reliable Incentive Mechanisms for Federated Learning in UAV-Enabled Internet of Vehicles
by Gangqiang Hu, Donglin Zhu, Jiaying Shen, Jialing Hu, Jianmin Han and Taiyong Li
Drones 2024, 8(10), 567; https://doi.org/10.3390/drones8100567 - 10 Oct 2024
Viewed by 826
Abstract
Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL [...] Read more.
Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively. Full article
(This article belongs to the Special Issue Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems)
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20 pages, 11786 KiB  
Article
Dark-SLAM: A Robust Visual Simultaneous Localization and Mapping Pipeline for an Unmanned Driving Vehicle in a Dark Night Environment
by Jie Chen, Yan Wang, Pengshuai Hou, Xingquan Chen and Yule Shao
Drones 2024, 8(8), 390; https://doi.org/10.3390/drones8080390 - 12 Aug 2024
Viewed by 1120
Abstract
Visual Simultaneous Localization and Mapping (VSLAM) is significant in unmanned driving, being is used to locate vehicles and create environmental maps, and provides a basis for navigation and decision making. However, in inevitable dark night environments, the SLAM system still suffers from a [...] Read more.
Visual Simultaneous Localization and Mapping (VSLAM) is significant in unmanned driving, being is used to locate vehicles and create environmental maps, and provides a basis for navigation and decision making. However, in inevitable dark night environments, the SLAM system still suffers from a decline in robustness and accuracy. In this regard, this paper proposes a VSLAM pipeline called DarkSLAM. The pipeline comprises three modules: Camera Attribute Adjustment (CAA), Image Quality Enhancement (IQE), and Pose Estimation (PE). The CAA module carefully studies the strategies used for setting the camera parameters in low-illumination environments, thus improving the quality of the original images. The IQE module performs noise-suppressed image enhancement for the purpose of improving image contrast and texture details. In the PE module, a lightweight feature extraction network is constructed and performs pseudo-supervised training on low-light datasets to achieve efficient and robust data association to obtain the pose. Through experiments on low-light public datasets and real-world experiments in the dark, the necessity of the CAA and IQE modules and the parameter coupling between these modules are verified, and the feasibility of DarkSLAM is finally verified. In particular, the scene in the experiment NEU-4am has no artificial light (the illumination in this scene is between 0.01 and 0.08 lux) and the DarkSLAM achieved an accuracy of 5.2729 m at a distance of 1794.33 m. Full article
(This article belongs to the Special Issue Trends in Embodied-Intelligence Unmanned Vehicle Technology and Applications of Intelligent Transport Systems)
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