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Drones
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Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems
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Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 7057

Special Issue Editors

Dr. Chao Huang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: human–machine collaborative control; decision making; path planning; fault-tolerant control with the application of automated vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Wang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: intelligent connected vehicles; vehicle system dynamic; active safety control
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaojian Li

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA
2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA
Interests: robotics and autonomous vehicles; intelligent transportation system; reinforcement learning; vehicle dynamics; optimal control
Special Issues, Collections and Topics in MDPI journals
Dr. Henglai Wei

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Interests: distributed control and optimization; secure and resilient control
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongxu Hu

E-Mail Website
Guest Editor
Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Interests: human-machine collaboration; human cyber-physical system; human digital twin

Special Issue Information

Dear Colleagues,

The future of transportation will be a three-dimensional network composed of unmanned aerial vehicles (UAVs) and self-driving vehicles (SDVs). The modeling of complex systems, state estimation, and intelligent control are crucial elements in the realm of intelligent transportation systems (ITSs). Modeling involves the precise abstraction of various factors such as dynamics, mechanical characteristics, and electrical systems.

Performing state estimation for UAVs and SDVs involves accurately determining current traffic conditions, while trajectory prediction anticipates future paths based on dynamic modeling and sensor data. Intelligent control employs advanced algorithms and decision systems to enable UAVs and SDVs to adapt in real ime to changes in their external environments.

These technologies are paramount for ITSs. Firstly, precise system modeling provides a theoretical foundation for ITSs, allowing them to better understand and adapt to intricate working environments. Secondly, state estimation offers accurate self-condition information, crucial for autonomous navigation and environmental perception. Lastly, intelligent control systems empower vehicles to make intelligent decisions based on real-time situations, enhancing efficiency and safety during operation.

In the evolution of ITSs, the integrated application of these technologies enhances vehicle autonomy, adaptability, and intelligence. Through modeling, state estimation, and intelligent control, UAV and SDV can better navigate complex traffic scenarios, improve driving safety, and lay a solid foundation for realizing fully autonomous driving and ITSs in the future.

This Special Issue aims to explore the modeling theories and methods for UAV and SDV in intelligent transportation systems. Further, the evolutionary mechanisms of the system are characterized by direct measurements versus indirect estimates and short-term predictions. Finally, advanced control algorithms are built based on models and data to enhance the safety and intelligence of transportation.

In terms of journal scope, this topic has significant relevance to signal processing, data fusion, and control—all issues within this journal’s focus. Signal processing involves handling diverse data from various sensors to extract crucial information about the states of UAV and SDV and the surrounding environment. Data fusion entails integrating information from multiple sources to obtain a more comprehensive and accurate representation of traffic conditions. Research in control involves designing intelligent algorithms to ensure UAV and SDV can respond appropriately to various conditions, thereby enhancing driving safety.

Topics including but not limited to the following:

  • Application of Artificial intelligence for unmanned aerial vehicles and self-driving vehicles;
  • Modeling, simulation, and dynamic analysis of the collaboration system for unmanned aerial vehicles and self-driving vehicles;
  • Unmanned aerial vehicle and self-driving vehicle decision making in a complex urban traffic environment;
  • Parameter identification and state estimation of unmanned aerial vehicles and self-driving vehicles;
  • Trajectory prediction and its application to intelligent transportation systems;
  • Human–machine shared control self-driving vehicles;
  • Coordinated control and fault-tolerant control of unmanned aerial vehicles and self-driving vehicles;
  • Advanced control for critical components of self-driving vehicles (e.g. chassis, engine, braking system, steering system, etc.);
  • Failure monitoring and protection of unmanned aerial vehicles and self-driving vehicles (eg. electromagnetic interference, actuator failure, etc.);
  • Design of new sensors and novel estimation and data fusion algorithms for unmanned aerial vehicles and self-driving vehicles.

Dr. Chao Huang
Dr. Yan Wang
Dr. Zhaojian Li
Dr. Henglai Wei
Dr. Zhongxu Hu
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

  • cooperative use of drones
  • flight dynamics
  • intelligent transportation systems
  • decision making
  • machine learning
  • transportation system dynamics
  • state estimation and parameter identification
  • active safety control
  • trajectory prediction
  • intelligent connected vehicles

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

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Research

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18 pages, 5277 KiB  
Article
A Unified Collision Avoidance Trajectory Planning with Dual Variables for Collaborative Aerial Transportation Systems
by Yi Chai, Xiao Liang and Jianda Han
Drones 2024, 8(11), 637; https://doi.org/10.3390/drones8110637 - 1 Nov 2024
Viewed by 571
Abstract
As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the [...] Read more.
As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs. Full article
(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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34 pages, 5375 KiB  
Article
Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design
by Maaz Ali Awan, Yaser Dalveren, Ali Kara and Mohammad Derawi
Drones 2024, 8(9), 440; https://doi.org/10.3390/drones8090440 - 28 Aug 2024
Viewed by 705
Abstract
This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of [...] Read more.
This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions. Full article
(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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24 pages, 785 KiB  
Article
Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles
by Jianlin Chen, Yang Xu and Zixuan Zheng
Drones 2024, 8(6), 273; https://doi.org/10.3390/drones8060273 - 20 Jun 2024
Cited by 1 | Viewed by 991
Abstract
In this paper, we explore the problem of smooth braking at preset points in autonomous vehicles using model predictive control (MPC) with a receding horizon extended state observer (RHESO) and a neural network (NN). An NN-based modeling method is proposed to intuitively describe [...] Read more.
In this paper, we explore the problem of smooth braking at preset points in autonomous vehicles using model predictive control (MPC) with a receding horizon extended state observer (RHESO) and a neural network (NN). An NN-based modeling method is proposed to intuitively describe the relationship between vehicle speed and the vehicle controllers (brake and throttle), and establish a dynamic model of autonomous vehicles. A sufficient condition is put forward to guarantee the convergence of the proposed NN. Furthermore, a composite MPC strategy based on RHESO is designed, which optimizes a given cost function over the receding horizon while mitigating the effects of modeling inaccuracies and disturbances. Additionally, easily verifiable conditions are provided to ensure the autonomous driving vehicles’ uniform boundedness. Numerically illustrative examples are given to demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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15 pages, 52659 KiB  
Article
CooPercept: Cooperative Perception for 3D Object Detection of Autonomous Vehicles
by Yuxuan Zhang, Bing Chen, Jie Qin, Feng Hu and Jie Hao
Drones 2024, 8(6), 228; https://doi.org/10.3390/drones8060228 - 29 May 2024
Cited by 1 | Viewed by 1095
Abstract
Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity, [...] Read more.
Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity, field-of-view limitations, and occlusion. To address these issues and enhance the perception capabilities of autonomous driving systems, we explore the concept of multi-vehicle multimedia cooperative perception by investigating the fusion of LiDAR point clouds and camera images from multiple interconnected vehicles with different positions and viewing angles. Specifically, we introduce a semantic point cloud feature-level cooperative perception framework, termed CooPercept, designed to mitigate computing complexity and reduce turnaround time. This is crucial, as the volume of raw sensor data traffic generally far exceeds the bandwidth of existing vehicular networks. Our approach is validated through experiments conducted on synthetic datasets from KITTI and OPV2V. The results demonstrate that our proposed CooPercept model surpasses comparable perception models, achieving enhanced detection accuracy and greater detection robustness. Full article
(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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Review

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33 pages, 1363 KiB  
Review
A Systematic Survey of Transformer-Based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends
by Minling Zhu, Yadong Gong, Chunwei Tian and Zuyuan Zhu
Drones 2024, 8(8), 412; https://doi.org/10.3390/drones8080412 - 22 Aug 2024
Viewed by 2405
Abstract
In recent years, with the continuous development of autonomous driving technology, 3D object detection has naturally become a key focus in the research of perception systems for autonomous driving. As the most crucial component of these systems, 3D object detection has gained significant [...] Read more.
In recent years, with the continuous development of autonomous driving technology, 3D object detection has naturally become a key focus in the research of perception systems for autonomous driving. As the most crucial component of these systems, 3D object detection has gained significant attention. Researchers increasingly favor the deep learning framework Transformer due to its powerful long-term modeling ability and excellent feature fusion advantages. A large number of excellent Transformer-based 3D object detection methods have emerged. This article divides the methods based on data sources. Firstly, we analyze different input data sources and list standard datasets and evaluation metrics. Secondly, we introduce methods based on different input data and summarize the performance of some methods on different datasets. Finally, we summarize the limitations of current research, discuss future directions and provide some innovative perspectives. Full article
(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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