Milestones in Autonomous Driving and Intelligent Vehicles Part \uppercase\expandafter{\romannumeral1}: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part \uppercase\expandafter{\romannumeral1} for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part \uppercase\expandafter{\romannumeral2} for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part \uppercase\expandafter{\romannumeral2}, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.Comment: 18 pages, 4 figures, 3 table

Fast and accurate trajectory tracking control of an autonomous surface vehicle with unmodeled dynamics and disturbances

In this paper, fast and accurate trajectory tracking control of an autonomous surface vehicle (ASV) with complex unknowns including unmodeled dynamics, uncertainties and/or unknown disturbances is addressed within a proposed homogeneity-based finite-time control (HFC) framework. Major contributions are as follows: (1) In the absence of external disturbances, a nominal HFC framework is established to achieve exact trajectory tracking control of an ASV, whereby global finitetime stability is ensured by combining homogeneous analysis and Lyapunov approach; (2) Within the HFC scheme, a finite-time disturbance observer (FDO) is further nested to rapidly and accurately reject complex disturbances, and thereby contributing to an FDO-based HFC (FDO-HFC) scheme which can realize exactness of trajectory tracking and disturbance observation; (3) Aiming to exactly deal with complicated unknowns including unmodeled dynamics and/or disturbances, a finite-time unknown observer (FUO) is deployed as a patch for the nominal HFC framework, and eventually results in an FUO-based HFC (FUOHFC) scheme which guarantees that accurate trajectory tracking can be achieved for an ASV under harsh environments. Simulation studies and comprehensive comparisons conducted on a benchmark ship demonstrate the effectiveness and superiority of the proposed HFC schemes

Editorial messages

Dear readers, The goal of this special edition was to shed light on the application of machine learning and artificial intelligence in the transformer industry, contributing to a better understanding of the requirements and available solutions. Expectations from these technologies are high in terms of what they can provide in various fields: monitoring, diagnostics, control, maintenance, perhaps even design, etc. A particular advantage of AI and ML technologies is the ability to predict future conditions, which opens up space for completely new paradigms, especially in maintenance. AI and ML technologies are also highly related to digitalization as a dominant global trend, which facilitates agile business models that respond to challenges within emerging markets. Digitalization also leads to a surge in data generation and accumulation, and with proper analysis, these data are expected to significantly secure and improve the grid performance and resolve various customer demands. This is also why we need solutions for understanding the data and learning from it. In addition, the speed and reliability of obtained information become essential, so all these trends are greatly supporting each other. Therefore, significant growth in investments and businesses related to this field is expected in near future. However, there are also challenges such as testing, deployability, scalability, transparency, affordability, and cyber security. I’m glad that a group of great authors together with our respectable Guest Editorial team have prepared high-quality articles this issue brings, addressing the above-mentioned key aspects. I hope you will enjoy your reading

Editorial messages

Dear readers, The goal of this special edition was to shed light on the application of machine learning and artificial intelligence in the transformer industry, contributing to a better understanding of the requirements and available solutions. Expectations from these technologies are high in terms of what they can provide in various fields: monitoring, diagnostics, control, maintenance, perhaps even design, etc. A particular advantage of AI and ML technologies is the ability to predict future conditions, which opens up space for completely new paradigms, especially in maintenance. AI and ML technologies are also highly related to digitalization as a dominant global trend, which facilitates agile business models that respond to challenges within emerging markets. Digitalization also leads to a surge in data generation and accumulation, and with proper analysis, these data are expected to significantly secure and improve the grid performance and resolve various customer demands. This is also why we need solutions for understanding the data and learning from it. In addition, the speed and reliability of obtained information become essential, so all these trends are greatly supporting each other. Therefore, significant growth in investments and businesses related to this field is expected in near future. However, there are also challenges such as testing, deployability, scalability, transparency, affordability, and cyber security. I’m glad that a group of great authors together with our respectable Guest Editorial team have prepared high-quality articles this issue brings, addressing the above-mentioned key aspects. I hope you will enjoy your reading

IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human–robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has increased since the appearance of recent machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as graphics processing units (GPUs), has made numerous robotic applications feasible which were not possible previously

Guest Editorial Special Issue on Integrated Sensing and Communication-Part I

Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC)

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Milestones in autonomous driving and intelligent vehicles: survey of surveys

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future