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Computing Paradigms and Security Issues in Connected and Autonomous Driving Vehicles
Published in B. B. Gupta, Megha Quamara, Internet of Things Security, 2020
Intelligent automation or automated driving technology involves embedded decision-making over the processed data or information, which in turn is based on inherent artificial intelligence (AI). Although the simulation of the complete intellectual proficiency of the human brain will take a long time, still it is possible to map some of the cognitive activities of humans to machine intelligence for the formulation of driving cognition. Gartner made a survey according to which, as of mid-2017, more than 46 companies are developing AI-based software for controlling the functionality of autonomous vehicles [50]. AI utilizes audio cognition, visual cognition, concentration, consciousness, thinking, and decision-making capabilities of the human brain. It serves three core functionalities in automated driving – sensing (involves environmental perception and passing the information through highly powerful computational devices to generate an environmental model that includes details about the road, obstacles, and pedestrians), mapping (involves updating the environmental view with the current one in a real-time fashion), and driving policy (embedded into the driving platform through which the vehicle is capable of informing the situation of the surroundings on the basis of which strategy is built and decisions are made) [51]. Sensor information and intelligent decision-making are decoupled in order to prevent the number and position of sensors putting any direct impact on the decision-making.
Autonomous Inspection and Maintenance with Artificial Intelligence Infiltration
Published in Diego Galar, Uday Kumar, Dammika Seneviratne, Robots, Drones, UAVs and UGVs for Operation and Maintenance, 2020
Diego Galar, Uday Kumar, Dammika Seneviratne
AVs involve the application of intelligent automation. In general, automation is defined as technology that actively selects data, transforms information, and makes decisions or controls processes. The decision-making process employed in the technology is based on inherent AI, hence the term “intelligent automation.” The transportation industry is only one among many industries that are increasingly influenced by automation involving AI. Intelligent, personal robots have begun to noticeably appear in diverse application fields ranging from home automation to medical assistance devices.
Accountability Increases Resource Sharing: Effects of Accountability on Human and AI System Performance
Published in International Journal of Human–Computer Interaction, 2021
Gabriel A. León, Erin K. Chiou, Adam Wilkins
Artificial intelligence (AI) has been defined as the ability for machines to perform functions that mimic higher forms of human cognition, including perception, reasoning, decision-making, learning, social functioning, and adaptability (Rai et al., 2019). Within this generic definition of AI, artificially intelligent automation, or AI automation, refers to AI designed specifically to accomplish functions that were formerly completed by humans, usually with greater efficiency, safety, accuracy, and speed. AI automation subsumes the characteristics of traditional automation – being able to collect and analyze data independently, interact mechanically or electronically with task environments, and provide information to human operators (Sheridan, 2002). Systems that incorporate AI automation are often mediated through an interface for human-machine communication (Christiaanse et al., 2014). In such systems, AI automation does not simply replace human activity, but instead has the potential to fill unique roles, and to provide powerful new functions, whether it is taking on low-risk tasks when there are a shortage of trained workers, or expanding capacity for information processing. Importantly, AI is often characterized as having the ability to act on its environment, including interacting with other agents. The social decision processes that ensue, in environments involving human counterparts, must be carefully designed and managed to ensure sustained system performance.
Open manufacturing: a design-for-resilience approach
Published in International Journal of Production Research, 2020
The linkage of service and manufacturing has been pursued for over a decade in different contexts. Feng, Sun, and He (2009) and Gao and Zhao (2012) used the term service-oriented manufacturing to emphasise integration of manufacturing and allied services. A customer-centric view of manufacturing was emphasised. A framework combining a multi-agent system with a service-oriented architecture for the development of intelligent automation control and execution systems was proposed by Giret and Botti (2010). Helo, Phuong, and Hao (2019) discussed the requirements for scheduling as-a-service in a cloud-based environment. Service composition is central to a cloud manufacturing platform. Yuan et al. (2020) discussed details of the hierarchical structure of a cloud manufacturing service.
Design of light weight exact discrete event system diagnosers using measurement limitation: case study of electronic fuel injection system
Published in International Journal of Systems Science, 2018
Piyoosh Purushothaman Nair, Santosh Biswas, Arnab Sarkar
With the growth in technology and larger scales of production, intelligent automation systems have found widespread usage in safety-critical applications across all domains of engineering, ranging from avionics and automobiles to industrial processes, manufacturing and electronic systems. In general, safety-critical systems must adhere to strict specifications on the operation of its critical components. With the rise in complexity of these systems, there has also been an increase in the number of faults occurring in them. Therefore, the properties such as robustness (measures insensitivity to disturbances when the system is in operation Zhang & Van Luttervelt, 2011), fault tolerance (enables the system to continue its operation, possibly at a reduced performance level, even in the presence of failures Koren & Krishna, 2010) and fault resilience (enables the system to quickly recover from failures Zhang & Van Luttervelt, 2011) have to be ensured for these systems in the presence of failures. Now, enforcement of these properties can only be achieved through the incorporation of safe design methodologies which enable efficient active monitoring and detection of unsafe execution states, whenever the system behaviour deviates from its stipulated specification.