Relevant bibliographies by topics / AI control safety

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI control safety'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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Journal articles on the topic "AI control safety"

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Abu, N. S., W. M. Bukhari, M. H. Adli, Hari Maghfiroh, and Alfian Ma’arif. "Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments." Journal of Robotics and Control (JRC) 5, no. 3 (2024): 613–35. https://doi.org/10.18196/jrc.v5i3.21114.

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This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.
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Jonsson, Roberth. "Avoiding AI Accidents." Industrial Vehicle Technology International 28, no. 1 (2020): 10. http://dx.doi.org/10.12968/s1471-115x(23)70471-6.

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Sharma,, Siddharth. "Revolutionizing Sports Bikes with Artificial Intelligence: Safety, Performance, and Design Innovations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30618.

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This study examines how artificial intelligence (AI) is incorporated into sports bikes and examines the significant impacts this has on ride quality, user experience, and safety. Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability. Furthermore, bike design processes are revolutionized by AI-driven design techniques, which allow for quick iterations and customisation. This study offers insights into how artificial intelligence (AI) can revolutionize sports bike technology in the future. Keywords: Artificial Intelligence (AI), Sports Bikes, Ride Quality, User Experience, Safety, Adaptive Cruise Control, Collision Avoidance Systems, Rider Safety, Engine Performance Improvements, Predictive Maintenance Algorithms, Bike Performance, Dependability, AI Driven Design Techniques.
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Krainiuk, O., Yu Buts, N. Didenko, V. Barbashyn, and O. Trishyna. "METROLOGICAL CONTROL OF SENSORS FOR MONITORING WORKING CONDITIONS USING ARTIFICIAL INTELLIGENCE." Municipal economy of cities 3, no. 184 (2024): 216–22. http://dx.doi.org/10.33042/2522-1809-2024-3-184-216-222.

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Metrological control plays a vital role in ensuring the accuracy and reliability of the data collected as part of the working conditions monitoring. It helps to prevent potential errors and guarantee the quality of the results, which is critical for the efficient assessment and management of occupational health and safety. The article aims to investigate and analyse the role and importance of metrological control of sensors in the system for monitoring working conditions at production facilities using artificial intelligence. The article examines the possibilities of using artificial intelligence (AI) to optimise metrological control and analysis of sensor data. The authors provide specific applications of AI to improve the metrological control of sensors and identify the advantages and challenges of introducing AI into the metrological control system at production facilities. These tasks will help to reveal the essence and potential of using AI in the metrological control of sensors for monitoring working conditions and emphasise its significance in improving the safety of workers. Using artificial intelligence to improve the accuracy of sensor measurements in monitoring working conditions helps to increase the efficiency and safety of production processes and reduce health risks for employees. The metrological control methodology is essential for ensuring the reliability of sensor and measuring device measurements. Applying machine learning algorithms to develop sensor calibration models can automate and optimise the processes of measuring working conditions, improving the accuracy and reliability of data. The proposed flowchart demonstrates an innovative approach to calibrating a sound level meter using artificial intelligence (AI). The results show that integrating AI into the occupational health and safety management system contributes to monitoring process automation, predicting risks and hazards to employee health, and optimising safety processes. These approaches can enhance the production processes’ efficiency, safety, and productivity. Keywords: measuring instruments, production environment, hazards, inspection, calibration.
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Duarte, Ana Beatriz. "THE CASE FOR CONVERGENCE IN AI SAFETY." IA Policy Brief Series 11, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.15073811.

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This policy brief emphasizes the need for a unified, evidence-based approach to AI safety. Given the inability to fully explain AI decisions, the global community has a growing consensus about the need to prioritize AI safety. However, discussions about AI safety often focus on different topics. While some scholars are alert to the dangers of cyberattacks and even the possibility of AI taking over, civil society experts claim for now-and-here issues such as the military use of autonomous weapons and other present-day risks. Common to the discourse of both groups is the concern with control, diverging in what and how.
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Kamala Aliyeva, Rashid Jafarov, Kamala Aliyeva, Rashid Jafarov. "SAFETY CONTROL SYSTEMS FOR ETHYLENE PRODUCTION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 35, no. 12 (2023): 206–13. http://dx.doi.org/10.36962/pahtei35122023-206.

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Ethylene production is a cornerstone of the petrochemical industry, with a global demand that continues to rise. Ensuring efficient, safe, and environmentally responsible ethylene production processes is paramount. Ethylene production control systems, encompassing a range of technologies and strategies, play a pivotal role in achieving these objectives. This abstract provides a concise overview of key aspects of ethylene production control systems, their challenges, and their critical importance. Ethylene production control systems serve as the backbone of modern ethylene plants, orchestrating complex operations to meet production targets while maintaining product quality. These systems integrate advanced technologies such as distributed control systems (DCS), safety instrumented systems (SIS), and process optimization tools to manage variables like temperature, pressure, flow rates, and feedstock composition. Efficiency and yield optimization are central objectives in ethylene production control. Control strategies are designed to maximize product output while minimizing energy consumption and raw material wastage. Additionally, safety control systems are a crucial component, mitigating risks associated with the highly flammable nature of ethylene and ensuring the well-being of personnel and environmental protection. Challenges in ethylene production control systems include managing the variability in feedstock quality, adapting to changing market demands, and adhering to stringent environmental regulations. As feedstock composition can fluctuate, control systems must continually adjust to maintain product quality and consistency. The emergence of Artificial Intelligence (AI) has brought new dimensions to ethylene production control. AI-driven predictive maintenance, anomaly detection, and process optimization are being explored to enhance operational efficiency and reduce downtime. In summary, ethylene production control systems are pivotal in sustaining the global supply of ethylene, a vital chemical compound. Their roles extend beyond production to safety and environmental compliance, making them indispensable components in the petrochemical industry's pursuit of efficiency and sustainability. To meet growing demands and address evolving challenges, the integration of advanced technologies, such as AI, holds promise for the future of ethylene production control systems. A strong safety culture is the backbone of safety control in ethylene production. This involves fostering a work environment where safety is paramount, and all personnel are equipped with the knowledge, awareness, and commitment to safety objectives. Clear communication and continuous training ensure that safety becomes an integral part of the organizational DNA. These systems represent a synergy of hardware, software, and operational protocols meticulously designed to monitor, detect, and respond to anomalous conditions, with a primary objective of averting incidents that could culminate in equipment damage, environmental harm, or harm to personnel. Keywords: Ethylene Production, Control Systems, Petrochemical Industry, Efficiency, Safety, Environmental Responsibility.
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Ourzik, Victoria Yousra. "Security and safety concerns in the age of AI." International Conference on AI Research 4, no. 1 (2024): 329–37. https://doi.org/10.34190/icair.4.1.3142.

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Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive data and control critical operations, securing them against such risks is essential. A breach or failure could compromise not only privacy but also the integrity of critical infrastructures. On the other hand, AI safety extends beyond technical defenses to the broader societal implications of AI. Issues like algorithmic bias, ethical decision-making, and unintended consequences of AI systems highlight the risks to human well-being. As AI becomes more autonomous, its alignment with human values and societal norms becomes paramount. Furthermore, the existential risks posed by advanced AI—such as loss of control or unintended outcomes—raise profound questions about the future of human-AI coexistence. This article delves into real-world case studies of AI failures and near-misses, offering tangible insights into the potential consequences of unchecked AI growth. It also explores strategies for mitigating these risks, balancing the pursuit of innovation with the need for transparency, accountability, and ethical oversight. As we look to the future, international cooperation and robust regulatory frameworks are essential to managing AI’s growing influence. By examining both technical and ethical dimensions, this article equips readers with a comprehensive understanding of AI security and safety, urging a proactive approach to managing the risks and harnessing the potential of this powerful technology.
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Vishal Goyal, Kamal Sharma, Amit Jain,. "A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems." Journal of Electrical Systems 20, no. 4s (2024): 486–507. http://dx.doi.org/10.52783/jes.2060.

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Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these problems. To manage higher-level connectivity and automation, the paper analyses and implements key research. It also finds issues and recommends solutions. The latest driverless car ADAS improvements have dramatically increased passenger safety. These systems are safer and more automated using sensors and ECUs. Most ADAS have RADAR, cameras, ultrasonic, and LiDAR. This work uses AI/ML-enabled Predictive Maintenance modelling to improve ADAS safety and longevity. AI and ML in Advanced Driver Assistance Systems (ADAS) are significant vehicle safety and reliability advances. AI/ML-enabled predictive maintenance detects and fixes ADAS component faults. ADAS predictive maintenance using AI/ML can detect issues, improve driver safety, and boost vehicle efficiency. Advanced sensor arrays and control units are needed for adaptive cruise control, traffic sign recognition, and lane-keeping assistance. AI/ML algorithms discover issues and enable early interventions in predictive maintenance models. Predictive maintenance is examined utilizing classical machine learning, deep learning, and reinforcement learning. Integration of numerous AI/ML models, real-time data processing, customization based on vehicle usage patterns, scalability, and adaptability of predictive maintenance models to new ADAS technologies are research gaps.
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Schöning, Julius, and Hans-Jürgen Pfisterer. "Safe and Trustful AI for Closed-Loop Control Systems." Electronics 12, no. 16 (2023): 3489. http://dx.doi.org/10.3390/electronics12163489.

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In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
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Turchin, Alexey, David Denkenberger, and Brian Green. "Global Solutions vs. Local Solutions for the AI Safety Problem." Big Data and Cognitive Computing 3, no. 1 (2019): 16. http://dx.doi.org/10.3390/bdcc3010016.

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There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progress.
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Dissertations / Theses on the topic "AI control safety"

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Yürek, Markus. "GATA-Assistenten – En konceptvalidering av röststyrning i GATA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36453.

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GATA is a web-application used by truckdrivers transporting timber from assigned pick-up places in the forest to specific receiving locations. GATA is used to facilitate and ease the navigation, communication and planning behind each delivery. This create situations where GATA needs to be used while driving, this is both dangerous and illegal. To solve this problem a voice-controlled proof-of-concept application for the most common features used during driving was developed and given the name GATA-Assistenten. The application was designed with a concept of having a few basic features built upon a stable platform. Dialogflow was chosen as tool to create the AI-voice control and to get it integrated with Google Assistant. Two tracks were created, one for drivers to change the estimated time of arrival and one to get the status on the receiving sites. According to statics at least 0,5% of all traffic related accidents can be directly contributed to interacting with communication devices, this is without taking the unrecorded cases into account. The conclusion based on these statistics is that GATA-Assistenten can not only help avoid accidents but also save lives. If a company wants to invest in safety, it is paramount to use voice-control, however it is also important to do research and development on new technologies to find out and fix the causes behind accidents.<br>GATA är en webbapplikation som används av lastbilschaufförer vid frakt av timmer från skogarna till mottagningsplatser för att lättare navigera, kommunicera och planera körningar på uppdrag av SCA. Det innebär att chaufförerna ibland behöver använda GATA under färd, något som inte bara kan vara en trafikfara utan även är olagligt. För att lösa detta problem skall ett proof-of-concept på röststyrning utvecklas för några av de vanligaste funktionerna som används under färd i GATA. Röststyrningsapplikationen som fick namnet GATA-Assistenten designades utifrån ett koncept om att ha få grundläggande funktioner byggt på en stabil plattform. Dialogflow valdes som verktyg för att skapa en AI-baserad röststyrning med integration via Google Assistant. GATA- Assistenten bestod slutligen av två huvudspår, det ena för att ändra den av chauffören angivna ankomsttiden till mottagningsplatsen och det andra för att ta reda på aviseringsläge på mottagningsplatserna. Enligt statistik är minst 0,5% av alla olyckor direkt orsakade av en förares interaktion med någon form av kommunikationsutrustning, detta utan att ta mörkertalet i beaktande. Slutsatsen som kan tas av detta är att GATA-Assistenten kan hjälpa till att undvika olyckor och rädda liv. Vill ett företag satsa på säkerhet är röststyrning ett måste, om det finns en nollvision kring olyckor i trafiken måste dock mer forskning och utveckling läggas på ny teknik för att ta reda på samt åtgärda orsakerna bakom alla olyckor.
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Books on the topic "AI control safety"

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Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2019.

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Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2019.

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Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2020.

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Book chapters on the topic "AI control safety"

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Yazdi, Mohammad, Sidum Adumene, Daniel Tamunodukobipi, Abbas Mamudu, and Elham Goleiji. "Virtual Safety Engineer: From Hazard Identification to Risk Control in the Age of AI." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82934-5_5.

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Gruteser, Jan, David Geleßus, Michael Leuschel, Jan Roßbach, and Fabian Vu. "A Formal Model of Train Control with AI-Based Obstacle Detection." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43366-5_8.

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Roy, Madhu Bala, and Abhishek Roy. "Road Safety Using AI: Safety Index Formulation of Roads by Integrated Road and Road Traffic Violation Control System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8695-4_6.

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Bensalem, Saddek, Panagiotis Katsaros, Dejan Ničković, et al. "Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems." In Bridging the Gap Between AI and Reality. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46002-9_15.

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AbstractLearning-enabled autonomous systems (LEAS) use machine learning (ML) components for essential functions of autonomous operation, such as perception and control. LEAS are often safety-critical. The development and integration of trustworthy ML components present new challenges that extend beyond the boundaries of system’s design to the system’s operation in its real environment. This paper introduces the methodology and tools developed within the frame of the FOCETA European project towards the continuous engineering of trustworthy LEAS. Continuous engineering includes iterations between two alternating phases, namely: (i) design and virtual testing, and (ii) deployment and operation. Phase (i) encompasses the design of trustworthy ML components and the system’s validation with respect to formal specifications of its requirements via modeling and simulation. An integral part of both the simulation-based testing and the operation of LEAS is the monitoring and enforcement of safety, security and performance properties and the acquisition of information for the system’s operation in its environment. Finally, we show how the FOCETA approach has been applied to realistic continuous engineering workflowsfor three different LEAS from automotive and medical application domains.
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Van Ments, Laila, Jan Treur, Jan Klein, and Peter H. M. P. Roelofsma. "Developing a Safety and Security AI Coach: A Second-Order Adaptive Network Model of Shared Mental Models in Hospital Teamwork." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_2.

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Mokadem, Nisrine, Fakhra Jabeen, Jan Treur, H. Rob Taal, and Peter H. M. P. Roelofsma. "Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_5.

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Praveen, P. Blessington, and K. Sivanantham. "Ensuring Safety and Security in Control Area Network-Based Automotive Embedded Systems with Advanced Encryption Standard Method Using Cloud Technology." In The Intersection of 6G, AI/Machine Learning, and Embedded Systems. CRC Press, 2025. https://doi.org/10.1201/9781003540212-16.

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Yi, Fengchao, Gang Guo, and Guoxia Liu. "Research on Safety Management and Control of Power Enterprises Based on AI and Edge Computing Technology." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1439-5_9.

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Myklebust, Thor, Tor Stålhane, and Dorthea Mathilde Kristin Vatn. "Level of Automation and Autonomy." In SpringerBriefs in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80504-2_17.

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Abstract Understanding the distinction between automation and autonomy is crucial as these two levels of system control shape how AI approaches, risk assessments, and requirements are structured. Automated systems operate on predefined instructions, performing tasks within set boundaries, while autonomous systems dynamically adapt and learn, evolving with their environments. This chapter explores these differences, along with the standards, regulations, and safety considerations that govern both automated and autonomous systems across various applications.
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Weigl, Linn-Marie, Fakhra Jabeen, Jan Treur, H. Rob Taal, and Peter H. M. P. Roelofsma. "Learning for a Better Safety and Security Culture Within an Organization: Reducing the Risk in Communication with AI Coaching for Security Communication Through Cyberspace." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_8.

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Conference papers on the topic "AI control safety"

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S, Dhananjeyan, Ruby Angel T. G, and Naveen Kumar S. "Her Shield: Women Safety System using Voice AI." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984972.

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Hemalatha, S., S. Malatthi Sivasundaram, P. Nithya, S. Deepak, V. S. Udhayaragavan, and S. Yogesh Kumar. "AI-Powered Workplace Safety: Helmet and Face Detection using YOLO for Access Control." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035190.

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M, Shanmughapriya, V. Brindha Devi, Sundar Sriram S, Molesh Kumar S, and Nishanth S. "VeriDrone - A real time AI Enabled System to Monitor and Report Safety in Construction Environment." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780211.

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Pop, Mădălin-Dorin, Viorel Cărbune, and Viorica Sudacevschi. "Safety and Sustainability Indicators of Variable Speed Limit Control in Intelligent Driver Models." In 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET). IEEE, 2024. https://doi.org/10.1109/honet63146.2024.10822951.

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Sharmila, K. Soni, and K. Ramesh Chandra. "Predicting Adverse Interactions: A Comprehensive Review of AI-Driven Drug-Drug Interaction Models for Enhanced Patient Safety." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823221.

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Satone, Kalyani, Pranjali Ulhe, Chitra Dhawale, and Supriya Narad. "AI-powered wearables and devices for women’s safety." In INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES FOR SUSTAINABLE ENERGY MANAGEMENT AND CONTROL 2023: ITSEMC2023. AIP Publishing, 2024. https://doi.org/10.1063/5.0244721.

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Paramasivam, M. E., Sutharshana Perumal, and Hariharan Pathmanaban. "Revolutionizing Road Safety: AI-Powered Road Defect Detection." In 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC). IEEE, 2024. http://dx.doi.org/10.1109/parc59193.2024.10486759.

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Terra, Ahmad, Hassam Riaz, Klaus Raizer, Alberto Hata, and Rafia Inam. "Safety vs. Efficiency: AI-Based Risk Mitigation in Collaborative Robotics." In 2020 6th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2020. http://dx.doi.org/10.1109/iccar49639.2020.9108037.

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Hobbs, Kerianne L., and Bernard Li. "Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control." In AIAA SCITECH 2024 Forum. American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-2583.

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Zakharchenko, Viktor, Volodymyr Netrebskyi, Yaroslav Taraniuk, and Sergii Shevchuk. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING THE SAFETY AND EFFICIENCY OF NUCLEAR POWER." In 17th IC Measurement and Control in Complex Systems. VNTU, 2024. https://doi.org/10.31649/mccs2024.4-11.

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The author analyzes the key areas of application of artificial intelligence technologies in the energy sector. The main priorities for the application of modern technologies in energy supply systems were highlighted. We have also AI assistance with errors caused by human factors was studied. Separately, the possible use of artificial intelligence was considered application of artificial intelligence to assess the technical condition of a power transformer based on AI. The development of an AI-based serviceability index (SI) model is presented. The proposed method is aimed at simplifying, accelerating, and reducing errors. In the proposed approach, artificial intelligence evaluates the insulation system of power transformers based on oil quality, chromatographic dissolved gas analysis (DGA), and the condition of the paper insulation. The report also highlighted the challenges that complicating the rapid implementation of artificial intelligence.
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Reports on the topic "AI control safety"

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Mittelsteadt, Matthew. AI Verification: Mechanisms to Ensure AI Arms Control Compliance. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20190020.

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The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.
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2

Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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Niles, Kenneth, Emily Leathers, Joe Tom, et al. Leveraging artificial intelligence and machine learning (AI/ML) for levee culvert Inspections in USACE Flood Control Systems (FCS). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49210.

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Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic platform and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.
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Hicks, Jacqueline. Export of Digital Surveillance Technologies From China to Developing Countries. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.123.

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There is evidence to show that Chinese companies, with some state credit backing, are selling digital surveillance technologies to developing countries, which are then sometimes used in authoritarian practices. However, there is little direct evidence to show that surveillance technologies sold by Chinese companies have more authoritarian potential than the technologies sold by non-Chinese companies. Some researchers define “surveillance technologies” as including any form of digital infrastructure. There is data to show that developing country governments are contracting Chinese companies to build digital infrastructures. Other researchers define “surveillance technologies” as smart city projects. It is estimated that in 2019, Chinese smart city technologies have been purchased in over 100 countries worldwide. Other researchers look at more specific elements of smart cities: There are estimates that the “AI surveillance” components of smart cities have been purchased in 47-65 countries worldwide, and the “data integration” security platforms in at least 80 countries. None of these figures imply anything about how these technologies are used. The “dual use” nature of these technologies means that they can have both legitimate civilian and public safety uses as well as authoritarian control uses. There is evidence of some governments in Africa using Chinese surveillance technologies to spy on political opponents and arrest protesters. Some authors say that some Chinese smart city projects are actually not very effective, but still provide governments with a “security aesthetic”. Research also shows that Chinese smart city technologies have been sold mostly to illiberal regimes. However, in the wider context, there is also ample evidence of non-Chinese surveillance technologies contributing to authoritarian control in developing countries. There is also evidence that UK companies sell surveillance technologies to mostly illiberal regimes. Some reports consulted for this rapid review imply that Chinese surveillance technologies are more likely to be used for authoritarian control than those sold by non-Chinese companies. This analysis is largely based on circumstantial rather than direct evidence. They rely on prior judgements, which are themselves subject to ongoing enquiry in the literature: Almost all of the reports consulted for this rapid review say that the most important factor determining whether governments in developing countries will deploy a particular technology for repressive purposes is the quality of governance in the country. No reports were found in the literature reviewed of Chinese state pressure on developing countries to adopt surveillance technologies, and there were some anecdotal reports of officials in developing countries saying they did not come under any pressure to buy from Chinese companies.
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Dong, Jiqian, Runjia Du, Paul (Young Joun) Ha, Sikai Chen, and Samuel Labi. Development of AI-based and control-based systems for safe and efficient operations of connected and autonomous vehicles. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317571.

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