2,274 research outputs found
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Towards Transportation Digital Twin Systems for Traffic Safety and Mobility Applications: A Review
Digital twin (DT) systems aim to create virtual replicas of physical objects
that are updated in real time with their physical counterparts and evolve
alongside the physical assets throughout its lifecycle. Transportation systems
are poised to significantly benefit from this new paradigm. In particular, DT
technology can augment the capabilities of intelligent transportation systems.
However, the development and deployment of networkwide transportation DT
systems need to take into consideration the scale and dynamic nature of future
connected and automated transportation systems. Motivated by the need of
understanding the requirements and challenges involved in developing and
implementing such systems, this paper proposes a hierarchical concept for a
Transportation DT (TDT) system starting from individual transportation assets
and building up to the entire networkwide TDT. A reference architecture is
proposed for TDT systems that could be used as a guide in developing TDT
systems at any scale within the presented hierarchical concept. In addition,
several use cases are presented based upon the reference architecture which
illustrate the utility of a TDT system from transportation safety, mobility and
environmental applications perspective. This is followed by a review of current
studies in the domain of TDT systems. Finally, the critical challenges and
promising future research directions in TDT are discussed to overcome existing
barriers to realize a safe and operationally efficient connected and automated
transportation systems.Comment: 15 pages, 2 figures; corrected issue in author(s) fiel
Street Smart in 5G : Vehicular Applications, Communication, and Computing
Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.Peer reviewe
A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
The potential of digital twin technology is yet to be fully realized due to
its diversity and untapped potential. Digital twins enable systems' analysis,
design, optimization, and evolution to be performed digitally or in conjunction
with a cyber-physical approach to improve speed, accuracy, and efficiency over
traditional engineering methods. Industry 4.0, factories of the future, and
digital twins continue to benefit from the technology and provide enhanced
efficiency within existing systems. Due to the lack of information and security
standards associated with the transition to cyber digitization, cybercriminals
have been able to take advantage of the situation. Access to a digital twin of
a product or service is equivalent to threatening the entire collection. There
is a robust interaction between digital twins and artificial intelligence
tools, which leads to strong interaction between these technologies, so it can
be used to improve the cybersecurity of these digital platforms based on their
integration with these technologies. This study aims to investigate the role of
artificial intelligence in providing cybersecurity for digital twin versions of
various industries, as well as the risks associated with these versions. In
addition, this research serves as a road map for researchers and others
interested in cybersecurity and digital security.Comment: 60 pages, 8 Figures, 15 Table
Sim2real and Digital Twins in Autonomous Driving: A Survey
Safety and cost are two important concerns for the development of autonomous
driving technologies. From the academic research to commercial applications of
autonomous driving vehicles, sufficient simulation and real world testing are
required. In general, a large scale of testing in simulation environment is
conducted and then the learned driving knowledge is transferred to the real
world, so how to adapt driving knowledge learned in simulation to reality
becomes a critical issue. However, the virtual simulation world differs from
the real world in many aspects such as lighting, textures, vehicle dynamics,
and agents' behaviors, etc., which makes it difficult to bridge the gap between
the virtual and real worlds. This gap is commonly referred to as the reality
gap (RG). In recent years, researchers have explored various approaches to
address the reality gap issue, which can be broadly classified into two
categories: transferring knowledge from simulation to reality (sim2real) and
learning in digital twins (DTs). In this paper, we consider the solutions
through the sim2real and DTs technologies, and review important applications
and innovations in the field of autonomous driving. Meanwhile, we show the
state-of-the-arts from the views of algorithms, models, and simulators, and
elaborate the development process from sim2real to DTs. The presentation also
illustrates the far-reaching effects of the development of sim2real and DTs in
autonomous driving
Threat Modelling of IoT Systems Using Distributed Ledger Technologies and IOTA
Internet of Things has emerged as a key techno-logical enabler for broader socio-technical and socio-economic paradigms, such as smart cities and Circular Economy. However, IoT systems are characterised by constraints and limitations which in order to be overcome they need to be deployed in conjunction and in synergy with other emerging ICT. Distributed Ledger Technologies (DLT) can help overcome challenges pertaining to data immutability, timeliness and security. However, the use of DLT does not satisfactorily mitigate security risks and vulnerabilities per se and currently cybersecurity aspects of IoT systems are addressed in a fragmented way. Furthermore, the conflict between the resource demanding Blockchains and the highly constrained nature of IoT devices hinders implementation efforts of corresponding systems. We consider networked systems that comprise both IoT and DLT technologies via the prism of Intelligent Transportation Systems (ITS). We elicit a three-tier threat model identifying attack vectors at the Device, the Network and the DLT layers. The identified attacks are then ranked by using the DREAD ranking scheme. The use of the threat model is demonstrated on a novel proof-of-concept IoT networked system implemented using the IOTA Tangle distributed ledger, where it helps to critically appraise the design of the system against the most critical attacks. Furthermore, the developed system is among the first in the literature to demonstrate the synergy of IoT and DLT on actual constrained embedded devices. The performance evaluation provides insights showing that such systems can be efficient and suitable for real-life deployment
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