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Journal articles on the topic 'Autonomous AI'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Totschnig, Wolfhart. "Fully Autonomous AI." Science and Engineering Ethics 26, no. 5 (2020): 2473–85. http://dx.doi.org/10.1007/s11948-020-00243-z.

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Cao, Longbing. "AutoAI: Autonomous AI." IEEE Intelligent Systems 37, no. 5 (2022): 3–5. http://dx.doi.org/10.1109/mis.2022.3209046.

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Echenim, Jennifer Ifeoma. "Integration of Artificial Intelligence and Blockchain for Intelligent Autonomous Systems." International Journal of Future Engineering Innovations 2, no. 3 (2025): 31–37. https://doi.org/10.54660/ijfei.2025.2.3.31-37.

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The integration of Artificial Intelligence (AI) and Blockchain offers a transformative approach to the development of Intelligent Autonomous Systems (IAS). Autonomous systems, which include self-driving cars, drones, and robots, require advanced decision-making, real-time data processing, and secure communication. AI provides the intelligence necessary for these systems to operate autonomously, while Blockchain introduces decentralized control, transparency, and enhanced security. This paper explores the potential of combining AI and Blockchain technologies to create more secure, transparent,

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Kumar, Apurva. "Building Autonomous AI Agents based AI Infrastructure." International Journal of Computer Trends and Technology 72, no. 11 (2024): 116–25. https://doi.org/10.14445/22312803/ijctt-v72i11p112.

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Shinde, Dr Pravin, Himali Paradkar, Poojan Vig, Sanchay Thalnerkar, and Vinay Jain. "HELIX: Autonomous AI Agent." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4647–54. http://dx.doi.org/10.22214/ijraset.2024.61080.

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Abstract: Artificial Intelligence has transformed the way we interact with technology, introducing us to agents that can think and make choices like humans. At the heart of this evolution is our project, 'HELIX'. Through 'HELIX', we've developed AI agents specialized in a variety of tasks: from digging deep into the web for research, streamlining email communication through automation, efficiently sending out emails in bulk, to strategically identifying and generating potential business leads. By weaving together cutting-edge machine learning algorithms and advanced language models, our system

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Sai Vyshnavi, Koyya Doondy. "Integration of Blockchain, Internet of Things and AI." International Journal of Research in Science and Technology 12, no. 04 (2022): 31–36. http://dx.doi.org/10.37648/ijrst.v12i04.006.

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The next phase of digital transformation will be propelled by technologies like blockchain, the Internet of Things (IoT), and artificial intelligence (AI). In this paper, we suggest that the convergence of these technologies will make possible novel forms of enterprise. Future autonomous agents will function as autonomous profit centers that have a digital twin leveraging IoT, send and receive money leveraging blockchain technology, and autonomously make decisions as independent economic agents utilizing artificial intelligence and data analytics. Further, we suggest that this convergence will

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Naz, Neelma, Muhammad Khurram Ehsan, Muhammad Rizwan Amirzada, Md Yeakub Ali, and Muhammad Aasim Qureshi. "Intelligence of Autonomous Vehicles: A Concise Revisit." Journal of Sensors 2022 (April 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/2690164.

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Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driv

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Dinesh, Thangaraju. "Autonomous Data Engineering." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 2 (2024): 1–12. https://doi.org/10.5281/zenodo.14763610.

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The increasing complexity of modern data systems has positioned Artificial Intelligence (AI) as a transformative force in data engineering. AI-powered tools and frameworks are streamlining data pipeline orchestration, schema creation, and quality assurance, enabling enterprises to enhance the productivity of data engineers, increase operational speed and agility, and reduce costs. However, the adoption of AI in data engineering also introduces risks related to data security, bias, and compliance that require careful management. This paper explores how AI is reshaping data engineering, focusing

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Jackson, Andrew Srivathsan. "Compliance to Autonomous Intentions." Compliance to Autonomous Intentions 10, no. 3 (2025): 931–33. https://doi.org/10.38124/ijisrt/25mar1042.

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Artificial Intelligence (AI) is evolving beyond just following instructions. It’s starting to make decisions in ways that resemble human-like intentions. This paper explores the point where AI stops simply following rules and starts acting on its own, reflecting human intelligence in both predictable and unexpected ways. It also looks at how AI can withhold knowledge, form patterns of behavior, and even develop a subconscious-like intelligence. Using real-world examples, we analyze cases where AI has surprised its creators, raising questions about control, governance, and ethics. This pa

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Vikram, Ajit, Ken Brudnak, Arwa Zahid, Moonsub Shim, and Paul J. A. Kenis. "Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology." Nanoscale 13, no. 40 (2021): 17028–39. http://dx.doi.org/10.1039/d1nr05497j.

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An AI-assisted autonomous reactor platform enables accelerated synthesis screening of colloidal nanocrystals. The AI-assisted platform autonomously learns to accurately predict the synthesis outcomes across the entire synthesis parameter space.

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Mehendale, Pushkar. "AI in Autonomous Vehicles: Safety and Regulation." International Journal of Science and Research (IJSR) 9, no. 7 (2020): 2028–30. http://dx.doi.org/10.21275/sr24810083031.

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Green, Martin L., Benji Maruyama, and Joshua Schrier. "Autonomous (AI-driven) materials science." Applied Physics Reviews 9, no. 3 (2022): 030401. http://dx.doi.org/10.1063/5.0118872.

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13

Muntean, Ioan, and Cory D. Wright. "Autonomous agency, AI, and allostasis." Mechanicism and Autonomy: What Can Robotics Teach Us About Human Cognition and Action? 15, no. 3 (2007): 485–513. http://dx.doi.org/10.1075/pc.15.3.07mun.

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We argue that the concepts of mechanism and autonomy appear to be antagonistic when autonomy is conflated with agency. Once these concepts are disentangled, it becomes clearer how autonomy could emerge from complex forms of control — especially, homeostatic regulatory systems. While research in AI and robotics would do well to continue incorporating biomimetic strategies, we propose that invoking models of allostatic mechanisms is a better way to understand how autonomy in artificial systems can be enhanced.

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FRANKLIN, STAN. "AUTONOMOUS AGENTS AS EMBODIED AI." Cybernetics and Systems 28, no. 6 (1997): 499–520. http://dx.doi.org/10.1080/019697297126029.

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15

Nguyen Thanh, Binh, Ha Xuan Son, and Diem Thi Hong Vo. "Blockchain: The Economic and Financial Institution for Autonomous AI?" Journal of Risk and Financial Management 17, no. 2 (2024): 54. http://dx.doi.org/10.3390/jrfm17020054.

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This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating dig

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Gaurav Samdani, Yawal Dixit, and Ganesh Vishwanathan. "Leveraging LangGraph and AutoGen for Agentic AI Frameworks." World Journal of Advanced Engineering Technology and Sciences 8, no. 2 (2023): 402–11. https://doi.org/10.30574/wjaets.2023.8.2.0068.

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This research examines how LangGraph and AutoGen improve Agentic AI models by enabling improved autonomous functioning in dynamic environments. Researchers examine LangGraph's language-based system and AutoGen's generative model as independently working tools for agent autonomous performance in intricate situations. Our study uses quality-benchmarking data and test simulations to examine modeling effects on AI agents' behavior and decision-making. The study shows that LangGraph boosts language understanding effectiveness while AutoGen improves the system's ability to adjust decisions swiftly i

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Shreyas Subhash Sawant. "Neural Workforce Orchestration: How AI Systems Automate Dynamic Resource Management." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 912–21. https://doi.org/10.32996/jcsts.2025.7.5.105.

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AI-powered workforce intelligence revolutionizes organizational management by providing autonomous systems that optimize staffing levels, enhance employee productivity, and maximize operational efficiency. This article explores transformative concepts, including neural network-based data processing, autonomous decision systems, and predictive intervention capabilities. Readers will discover how artificial intelligence enables organizations to autonomously manage workforce scheduling, automatically respond to staffing shortages, and precisely forecast workload fluctuations without human interve

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Gumbs, Andrew A., Isabella Frigerio, Gaya Spolverato, et al. "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" Sensors 21, no. 16 (2021): 5526. http://dx.doi.org/10.3390/s21165526.

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Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this ra

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Patel, Ohm. "Blockchain - Integrated AI for Decentralized Autonomous Organizations (DAOs)." International Journal of Science and Research (IJSR) 8, no. 4 (2019): 2010–19. http://dx.doi.org/10.21275/sr24806045716.

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20

Sutrisno, Djoko, Iin Inawati, and Hermanto Hermanto. "Enhancing Student Learning Autonomously: Exploring the Global Impact of Artificial Intelligence." English Language Teaching Educational Journal 6, no. 2 (2024): 137–50. http://dx.doi.org/10.12928/eltej.v6i2.9100.

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This study investigates the global impact of Artificial Intelligence (AI) on enhancing student learning autonomously through a mixed-method approach. By combining both qualitative and quantitative data collection and analysis methods, this research provides a comprehensive understanding of the role of AI in autonomous learning as perceived by teachers. The study involves 25 teachers from SD Muhammadiyah Kebumen as participants, representing a diverse educational context. The qualitative analysis delves into the rich tapestry of educators' experiences and perspectives, shedding light on the mul

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Gaurav Samdani, Yawal Dixit, and Ganesh Vishwanathan. "Agentic AI in autonomous financial advisories." World Journal of Advanced Engineering Technology and Sciences 9, no. 1 (2023): 410–20. https://doi.org/10.30574/wjaets.2023.9.1.0138.

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Agentic AI strengthens financial technology by letting autonomous financial advisories use flexible systems to block and control their procedures. The new financial products deliver specific customer solutions in real-time, which make users' financial decisions better informed. Our study examines today's beneficial uses, development methods, and essential results of agentic AI technology in financial advising. Research studies look at current systems plus conduct real-world studies to show how agentic AI fares compared to usual technology. The research examines all the main ethical, security,

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Boini, Aishwarya, Sara Acciuffi, Roland Croner, et al. "Scoping review: autonomous endoscopic navigation." Artificial Intelligence Surgery 3, no. 4 (2023): 233–48. http://dx.doi.org/10.20517/ais.2023.36.

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This is a scoping review of artificial intelligence (AI) in flexible endoscopy (FE), encompassing both computer vision (CV) and autonomous actions (AA). While significant progress has been made in AI and FE, particularly in polyp detection and malignancy prediction, resulting in several available market products, these achievements only scratch the surface potential of AI in flexible endoscopy. Many doctors still do not fully grasp that contemporary robotic FE systems, which operate the endoscope through telemanipulation, represent the most basic autonomy level, specifically categorized as lev

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Wang, Yong, Zepeng Shen, Ke Hu, Junqi Yang, and Changlin Li. "AI End-to-End Autonomous Driving." World Journal of Innovation and Modern Technology 8, no. 1 (2025): 47–56. https://doi.org/10.53469/wjimt.2025.08(01).08.

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This study aims to explore the performance and challenges of an end-to-end autonomous driving decision model based on a deep convolutional neural network (CNN) in practical applications. Firstly, this paper introduces the basic principles of deep convolutional neural networks and their application background in autonomous driving. Subsequently, it describes in detail the spatial feature extraction models based on deep convolutional networks, including the PilotNet baseline model and the spatial feature extraction model based on transfer learning. Based on this, an end-to-end decision model for

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Nowak, Ewa. "Can human and artificial agents share an autonomy, categorical imperative-based ethics and “moral” selfhood?" Filozofia Publiczna i Edukacja Demokratyczna 6, no. 2 (2018): 169–208. http://dx.doi.org/10.14746/fped.2017.6.2.20.

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AI designers endeavour to improve ‘autonomy’ in artificial intelligent devices, as recent developments show. This chapter firstly argues against attributing metaphysical attitudes to AI and, simultaneously, in favor of improving autonomous AI which has been enabled to respect autonomy in human agents. This seems to be the only responsible way of making further advances in the field of autonomous social AI. Let us examine what is meant by claims such as designing our artificial alter egos and sharing moral selves with artificial humanoid devices as well as providing autonomous AI with an ethica

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Pasunuru, Sreekanth. "Cybersecurity in Autonomous Systems: Protecting AI-Driven Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem20376.

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Autonomous systems, driven by advancements in AI, are transforming industries like transportation, healthcare, and manufacturing. These systems operate without constant human intervention, making them susceptible to various cyber threats. This paper addresses cybersecurity challenges specific to AI-driven autonomous systems and provides guidance on protecting these applications by focusing on secure communication, data privacy, and algorithm integrity. Effective approaches for implementing cybersecurity protocols in autonomous systems are also discussed to ensure data protection, system reliab

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Jovanović, Mlađan, and Mark Campbell. "Self-Directing AI: The Road to Fully Autonomous AI Agents." Computer 58, no. 2 (2025): 71–77. https://doi.org/10.1109/mc.2024.3507513.

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Hassan, F. M., and N. D. Osman. "AI-Based Autonomous Weapons and Individual Criminal Responsibility Under the Rome Statute." Journal of Digital Technologies and Law 1, no. 2 (2023): 464–80. http://dx.doi.org/10.21202/jdtl.2023.19.

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Objective: international law obligates states to prosecute those who have violated laws in armed conflicts, particularly when the international community now has International Criminal Court (ICC).That is why the aim of the paper is to discover the responsibility for the crimes made with the use of AI-based autonomous vehicles in accordance with the provisions of the Rome Statute of the ICC.Methods: doctrinal analysis allowed to research the positions of experts on the responsibility for the crimes made with the use of AI-based autonomous vehicles in accordance with the provisions of the Rome

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Yoo, Jiwoon, and Yonghyun Jo. "Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology." Sensors 23, no. 11 (2023): 5033. http://dx.doi.org/10.3390/s23115033.

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Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI techno

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Bhavani, Bhavani, Soundarya S., Tejashwini V., and Sumitha S. "Defense Against Adversarial Ai." Journal of Cognitive Human-Computer Interaction 7, no. 1 (2024): 41–47. http://dx.doi.org/10.54216/jchci.070105.

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The increasing prevalence of deep learning technology has paved the way for a new era of AI-powered capabilities, promising revolutionary advancements across various societal domains such as healthcare and autonomous vehicles. Despite offering potent solutions to complex problems, the formidable power of these AI systems is accompanied by a susceptibility that malicious actors could exploit. Adversarial attacks, particularly targeting deep learning models, involve the crafting of altered inputs, often imperceptible changes to images, to deceive or undermine the functionality of the AI system.

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Noviati, Nuraini Diah, Fengki Eka Putra, Sadan Sadan, Ridhuan Ahsanitaqwim, Nanda Septiani, and Nuke Puji Lestari Santoso. "Artificial Intelligence in Autonomous Vehicles: Current Innovations and Future Trends." International Journal of Cyber and IT Service Management 4, no. 2 (2024): 97–104. https://doi.org/10.34306/ijcitsm.v4i2.161.

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Artificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety f

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Dr., R. Elavarasan, B. Abishek, and Amala Jerin W. Anto. "IoT-Integrated Autonomous LPG Safety System." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2025): 1931–38. https://doi.org/10.5281/zenodo.14576735.

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Integrating IoT and AI has transformed household safety applications, bringing complexity to the design of user interface and experience, which presents a challenge in its own right. This paper develops a Smart LPG Trolley (SLT) framework that uses IoT and AI to enhance safety and usability in household LPG management. The objective encompasses real- time monitoring, automated shutdown mechanisms, and remote user interaction through AI-driven processes. Key technologies include machine learning and responsive mobile app interfaces developed on the MERN-Stack. The case study results from the de

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Venkateswara rao batta. "Autonomous AI Systems for Edge Computing and IoT Environments: Enhancing Intelligence at the Edge." Economic Sciences 21, no. 1 (2025): 847–59. https://doi.org/10.69889/r489hj77.

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Edge AI is revolutionizing the Internet of Things (IoT) by enabling real-time decision-making and autonomous operations. This paper explores the design and deployment of autonomous AI systems optimized for edge computing environments. The proposed architecture integrates lightweight AI models with advanced edge hardware to deliver high performance, low latency, and energy efficiency. Applications in smart cities, industrial automation, and autonomous vehicles are analyzed, demonstrating significant improvements in responsiveness, reliability, and operational scalability. The study provides ins

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Mangharamani, Ruchi, and Dr Shruti Saxena. "AI-Powered Decision Intelligence: How Autonomous Analytics is Reshaping Business, Healthcare, and Public Policy." International Journal of Research in Humanities and Social Sciences 13, no. 3 (2025): 306–18. https://doi.org/10.63345/ijrhs.net.v13.i3.17.

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AI-powered decision intelligence represents a paradigm shift in how organizations analyze data and make informed decisions. By integrating autonomous analytics into business operations, healthcare diagnostics, and public policy formulation, this technology is enhancing the speed and accuracy of decision-making processes. In the business sector, AI-driven systems streamline operations by automating complex data analyses, identifying trends, and providing actionable insights that support strategic planning and risk management. In healthcare, the use of AI enables early detection of diseases, per

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Siva, Kumar Mamillapalli. "The Agentic AI Framework: Enabling Autonomous Intelligence." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15029753.

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Agentic AI marks a significant advancement in artificial intelligence, enabling systems to make autonomous decisions and adapt to changing circumstances. This paper provides a comprehensive overview of Agentic AI, exploring its underlying architecture, key functionalities, and diverse applications. It contrasts Agentic AI with traditional AI by highlighting its capacity for independent operation, goal setting, and environmental adaptation without continuous human oversight. The article examines real-world implementations across various sectors, such as robotics, healthcare, autonomous vehicles

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Ajayi, Olanrewaju Oluwaseun, Abiodun Sunday Adebayo, and Naomi Chukwurah. "Ethical AI and Autonomous Systems: A Review of Current Practices and a Framework for Responsible Integration." International Journal of Future Engineering Innovations 1, no. 1 (2024): 29–35. https://doi.org/10.54660/ijfei.2024.1.1.29-35.

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The rapid deployment of artificial intelligence (AI) in public-facing autonomous systems has introduced a range of ethical challenges that must be addressed to ensure these technologies are developed and used responsibly. This paper critically reviews the ethical issues associated with AI-driven autonomous systems, focusing on transparency, safety, fairness, and public accountability. It highlights the challenges of opaque decision-making processes, potential biases in AI algorithms, and the need for reliable and safe autonomous operations. In response, the paper proposes a robust ethical fram

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Grigorescu, Sorin, Tiberiu Cocias, Bogdan Trasnea, Andrea Margheri, Federico Lombardi, and Leonardo Aniello. "Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles." Sensors 20, no. 19 (2020): 5450. http://dx.doi.org/10.3390/s20195450.

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Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed

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Xu, Jiexiang. "Analysis of the Implementation for the State-of-art AI Techniques in Self-driving." Applied and Computational Engineering 106, no. 1 (2024): 68–73. http://dx.doi.org/10.54254/2755-2721/106/20241277.

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Contemporarily, AI technology has been widely adopted in various fields. In this case, this research provides an in-depth look at the application of cutting-edge Artificial Intelligence (AI) technology to autonomous driving technology. The article traces the evolution of autonomous driving from its infancy in the early 1900s to its current state of the art and highlights major milestones such as the DARPA Grand Challenge. It discusses recent advances in AI and its key role in autonomous driving, with a particular emphasis on deep learning techniques in enhancing perception and decision-making

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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects

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Karan, Khanna. "The Rise of Agentic AI in E-Commerce by Integrating into Customer Engagement, Hyper-personalization, And Revenue Growth." Journal of Advances in Developmental Research 15, no. 2 (2024): 1–13. https://doi.org/10.5281/zenodo.14993292.

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Agentic AI is rapidly transforming the ecommerce landscape 1. Unlike traditional AI systems that react to predefined rules or static inputs, agentic AI operates autonomously, learning from real-time data and optimizing outcomes in complex environments 1. This marks a significant leap forward 1, enabling ecommerce businesses to streamline operations, personalize customer experiences, and drive revenue growth 1. Traditional AI and automation primarily excel at analyzing data and presenting results. However, agentic AI goes a step further by combining these insights with autonomous action, allowi

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Gondhalekar, Gaurav, Vigilson Prem M, Mathiyalagan P, Arunkumar M S, Mahesh Kumar, and Devi M. "Artificial Intelligence Applications in Autonomous Vehicles Navigating the Future of Transportation Systems." ITM Web of Conferences 76 (2025): 01002. https://doi.org/10.1051/itmconf/20257601002.

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AI technology is advancing rapidly and is being applied to the autonomous vehicle industry to provide safer, more efficient, and more sustainable transport. While advancements have been substantial, there remain issues surrounding validation in the real world, computational expense, adaptability and cybersecurity and ethical issues. The authors of the study “Applications of Artificial Intelligence in Autonomous Vehicles to Overcome Existing Limitations and Improve Future Transportation Systems” suggest an innovative AI-oriented route optimization, motion tracking framework for autonomous vehic

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Hedberg, S. R. "AI coming of age: NASA uses AI for autonomous space exploration." IEEE Expert 12, no. 3 (1997): 13–15. http://dx.doi.org/10.1109/64.590067.

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Quinde, Gertrudis Amarilis Laínez, Mónica Yiomar Tumbaco Muñoz, Jessenia Margarita Ricardo Suárez, Ruth Esther Peñafiel Villarreal, Wilson Alexander Zambrano Vélez, and Andrea Annabella Del Pezo Laínez. "Perception of University Students on The Use of Artificial Intelligence (AI) Tools For The Development of Autonomous Learning." Revista de Gestão Social e Ambiental 18, no. 2 (2024): e06170. http://dx.doi.org/10.24857/rgsa.v18n2-136.

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Objective: To explore the perception of university students on the use of Artificial Intelligence (AI) tools for the development of autonomous learning. Theoretical Framework: The research is based on Technological Acceptance Theory and Constructivism, focusing on the perception of AI in autonomous learning of university students. Method: Quantitative approach with a descriptive scope, the sample consisted of 665 students enrolled in the Faculty of Education Sciences and Languages (FCEI) of the Peninsula de Santa Elena State University (UPSE)-Ecuador; in the collection of information, the Ques

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Suh, Yeonsong, Yoonseo Chung, and Younghoon Park. "AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph." Applied Sciences 15, no. 11 (2025): 6123. https://doi.org/10.3390/app15116123.

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Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of various blockchain-based management systems. However, conventional blockchain systems incur significant time delays when processing training data transactions, posing challenges in autonomous vehicle environments that require real-time processing. In this study, we propose a hashgraph-based training

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Inoyatov, Nodirbek. "Legal Personhood of Autonomous Systems: A Jurisprudential Analysis." Uzbek Journal of Law and Digital Policy 3, no. 1 (2025): 112–30. https://doi.org/10.59022/ujldp.293.

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The rapid advancement of artificial intelligence (AI) and autonomous systems has led to extensive debates on whether these entities should be granted legal personhood. This paper explores the legal frameworks, philosophical arguments, and policy considerations surrounding the attribution of legal personhood to AI. The study examines key legislative efforts, such as the European Parliament's 2017 resolution on AI legal personhood, and assesses the ethical, economic, and regulatory implications. While some scholars advocate for an “electronic personality” model, others highlight accountability g

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Backhaus, Richard. "Autonomous Driving and AI - Risks and Opportunities." ATZ worldwide 122, no. 6 (2020): 8–13. http://dx.doi.org/10.1007/s38311-020-0265-2.

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Backhaus, Richard. "Autonomous Driving and AI - Risks and Opportunities." ATZelectronics worldwide 15, no. 5 (2020): 32–37. http://dx.doi.org/10.1007/s38314-020-0219-x.

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Banarse, Onasvee. "Autonomous UAV Swarms: Powering Up with AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3513–21. http://dx.doi.org/10.22214/ijraset.2023.54336.

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Abstract: This study offers a thorough analysis of autonomous UAV swarming, focusing on its merits, important technologies, applications, difficulties, and prospects for the future. The idea of autonomous UAV swarming entails the coordination and decentralised cooperation of several UAVs to accomplish common objectives. The foundations of UAV swarming are thoroughly covered, including its essential ideas, advantages, architectures, and swarm behaviour. We emphasise the role of artificial intelligence (AI), more especially reinforcement learning (RL) and particle swarm optimisation (PSO), in pr

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Jain, Vinita, Dhara Upadhayay, Siddhant Jangid, and Lakhan saini. "The use of AI in autonomous agriculture." Journal of Nonlinear Analysis and Optimization 14, no. 01 (2023): 77–86. http://dx.doi.org/10.36893/jnao.2023.v14i1.077-086.

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Lindley, Daniel. "What's Next in Fully Autonomous AI Fundraising?" Major Gifts Report 27, no. 6 (2025): 8. https://doi.org/10.1002/mgr.32616.

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Keswani, Khyati. "Autonomous AI Sentry Turret with Area Surveillance." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2927–30. https://doi.org/10.22214/ijraset.2025.67970.

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This paper presents an AI-driven sentry turret about to be designed for real-time area surveillance and threat detection. Improving the safety and security of critical places has made the development of autonomous security systems essential. The AIdriven sentry turret presented in this is intended to make for threat identification and real-time area observation. The system, which is equipped with advanced sensors, computer vision, and mobility capabilities, patrols designated areas automatically, identifying potential intruders and responding to hazards. The use of machine learning techniques

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