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

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.

• 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