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Captain Peter H. Collins offers a deep and insightful consideration of the evolving role of automation and artificial intelligence in modern aviation. Drawing from his extensive experience as a 747-400 captain, Collins presents a nuanced analysis of how advancements in instrumentation and automation have dramatically enhanced flight safety and operational efficiency. At the same time, he critically examines the potential risks and limitations that come with the increasing reliance on automated systems, especially as the aviation industry moves closer to the possibility of fully automated passenger flights. In this compelling narrative, Collins leverages real-world examples and recent aviation incidents to underscore the crucial importance of maintaining a balanced approach that integrates both human intelligence and automated systems. While acknowledging the remarkable achievements of AI and automation in revolutionizing aviation, the book emphasizes that human expertise remains irreplaceable in certain critical situations. Collins argues that the over-reliance on automation without sufficient human oversight can lead to unforeseen challenges, making it imperative to ensure that pilots are well-equipped to intervene when necessary. Automation Max talks about the key areas of aviation, exploring how AI and automation have transformed flight operations, pilot training, and decision-making processes in the cockpit. He provides readers with an in-depth understanding of the benefits these technologies bring, such as enhanced precision, reduced pilot workload, and improved overall safety. However, he also highlights potential drawbacks, including the risk of pilot de-skilling and the ethical dilemmas associated with fully automated flights. The book is an enlightening read for aviation professionals, engineers, and anyone with an interest in the intersection of technology and human factors in aviation. Collins' firsthand experience as a seasoned captain lends credibility and authenticity to his analysis. Collins shares his insights on how automation is reshaping the role of pilots, the importance of continuous training to ensure pilots can effectively manage both routine and emergency situations, and the challenges of maintaining safety in an increasingly automated environment. He also explores the broader implications of these technological advancements for the aviation industry, including regulatory challenges, public perception, and the potential for future innovations. In addition to its technical insights, the book offers a broader discussion on the ethical and practical implications of AI and automation in aviation. Collins raises thought-provoking questions about the future of the industry, such as whether it is wise to pursue fully automated passenger flights, and what role human oversight should play in an era dominated by advanced technology. His analysis encourages readers to think critically about the balance between automation and human control, and to consider the long-term impacts of these changes on aviation safety and efficiency. Automation Max: Optimizing AI and Human Intelligence in Aviation by Peter H. Collins is a great read for those who want to stay ahead of the curve in understanding the future of aviation. By blending technical knowledge with real-world experience, Collins offers a comprehensive and balanced perspective on how AI and automation are transforming the aviation industry. This book is an invaluable resource for anyone looking to navigate the complexities of modern aviation and the critical role that human intelligence continues to play in ensuring safe and successful flights.

\"The military and police use robots to stop bombs from exploding. Read this book to discover how engineers create these life-saving machines.\"-- Provided by publisher.

With coverage ranging from the influence of professional identity in medicine and problematic nature of \"human error,\" to the psychological and social features that characterize healthcare work, to the safety-critical aspects of interfaces and automation, this book spans the width of the human factors field and its importance for patient safety today. In addition, the book discusses topics such as accountability, just culture, and secondary victimization in the aftermath of adverse events and takes readers to the leading edge of human factors research today: complexity, systems thinking, and resilience.

This publication provides specific recommendations on research reactor instrumentation and control systems and software important to safety, including instrumentation and control system architecture and associated components, from sensors to actuators, operator interfaces and auxiliary equipment, to meet the relevant requirements of IAEA Safety Standards Series No. SSR-3, Safety of Research Reactors. The recommendations and guidance apply to both the design and configuration management of instrumentation and control systems for new research reactors and the modernization of the instrumentation and control systems at existing research reactor facilities. In addition, this Safety Guide provides recommendations and guidance on human factors engineering and human-machine interfaces, and for computer based systems and software for use in instrumentation and control systems important to safety. This Safety Guide is a revision of IAEA Safety Standards Series No. SSG-37, which it supersedes.

Plug & Produce aims to revolutionize manufacturing by enabling seamless machine integration into production processes without extensive programming. This concept, leveraging multi-agent systems (MAS), offers increased flexibility and faster production ramp-up times after reconfiguration. As automated manufacturing moves towards greater human integration, this paper addresses safe operation within the Plug & Produce concept. The main safety challenge arises from autonomous decision-making, as agents in the MAS lack awareness of the risk consequences of their behavior. Additionally, the difficulty of perceiving the system’s exact behavior leads to the implementation of overly restrictive safety measures. This limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control to conduct automatic safety analysis and reason task allocations to avoid risks. The method’s benefits are the generation of control actions that comply with safety requirements during operation, eliminating the need for overly restrictive safety measures and allowing more effective equipment utilization. The method’s benefit is illustrated through a manufacturing scenario with two different configurations: one using a hazardous machine and the other using a less hazardous one. Formal verification using the model checker NuSMV demonstrated that safety requirements were satisfied in both configurations, without the need for manual modifications of the safety control system after reconfiguration. The results for this specific manufacturing scenario showed that there are more reachable states (20 states) in the safer second configuration, compared to the first configuration (16 states). This means that the presented control strategy dynamically adjusts the system’s behavior to confirm safety. Hence, this method maintains safety without fixed safety rules that limit the operations.

In recent years, vehicular ad hoc networks (VANETs) have emerged as a crucial component of intelligent traffic systems, offering enhanced road safety through autonomous, distributed, and dynamically structured communication. However, ensuring secure and privacy-preserving message broadcasting in VANETs remains a significant challenge due to their open-access nature. Existing solutions have addressed various security and privacy concerns, yet critical issues such as resistance to traffic analysis, unlinkability of messages, computational efficiency, and location privacy remain underexplored. To bridge these gaps, we propose a blockchain-based privacy-preserving scheme that strengthens VANET security while addressing unobservability, unlinkability, and efficiency in authentication. Our approach leverages a cache-based anonymizer server positioned between the On-Board Unit (OBU) and the Roadside Unit (RSU), which enhances privacy by masking communication patterns and improves efficiency by reducing authentication overhead. Performance evaluations demonstrate that our scheme significantly reduces computational costs, achieving 95.17% to 97.00% reduction in V2V and 97.81% to 98.90% reduction in V2RSU communication time compared to referenced schemes. Additionally, our approach reduces communication cost by 67.94% to 81.67% for V2V and 72.40% to 88.00% for V2RSU, while the location leakage probability is minimized to 0.05% which is significantly lower than centralized architectures. Furthermore, our scheme ensures strong privacy protection, attaining a maximum entropy level of 5 which is 95.8% higher than existing schemes. These results confirm that our framework minimizes computational overhead, optimizes communication efficiency, and enhances privacy protection, making it a robust and scalable solution for VANET systems.

To ensure optimal use of images while preserving privacy, it is necessary to partition the shared image into public and private areas, with public areas being openly accessible and private areas being shared in a controlled and privacy-preserving manner. Current works only facilitate image-level sharing and use common cryptographic algorithms. To ensure efficient, controlled, and privacy-preserving image sharing at the area level, this paper proposes an image partition security-sharing mechanism based on blockchain and chaotic encryption, which mainly includes a fine-grained access control method based on Attribute-Based Access Control (ABAC) and an image-specific chaotic encryption scheme. The proposed fine-grained access control method employs smart contracts based on the ABAC model to achieve automatic access control for private areas. It employs a Cuckoo filter-based transaction retrieval technique to enhance the efficiency of smart contracts in retrieving security attributes and policies on the blockchain. The proposed chaotic encryption scheme generates keys based on the private areas’ security attributes, largely reducing the number of keys required. It also provides efficient encryption with vector operation acceleration. The security analysis and performance evaluation were conducted comprehensively. The results show that the proposed mechanism has lower time overhead than current works as the number of images increases.

Automobile crashes are the seventh leading cause of death worldwide, resulting in over 1.25 million deaths yearly. Automated, connected, and intelligent vehicles have the potential to reduce crashes significantly, while also reducing congestion, carbon emissions, and increasing accessibility. However, the transition could take decades. This new handbook serves a diverse community of stakeholders, including human factors researchers, transportation engineers, regulatory agencies, automobile manufacturers, fleet operators, driving instructors, vulnerable road users, and special populations. The handbook provides information about the human driver, other road users, and human–automation interaction in a single, integrated compendium in order to ensure that automated, connected, and intelligent vehicles reach their full potential. Features Addresses four major transportation challenges—crashes, congestion, carbon emissions, and accessibility—from a human factors perspective Discusses the role of the human operator relevant to the design, regulation, and evaluation of automated, connected, and intelligent vehicles Offers a broad treatment of the critical issues and technological advances for the designing of transportation systems with the driver in mind Presents an understanding of the human factors issues that are central to the public acceptance of these automated, connected, and intelligent vehicles Leverages lessons from other domains in understanding human interactions with automation Sets the stage for future research by defining the space of unexplored questions

The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems.