Integrating artificial intelligence in unmanned vehicles: navigating uncertainties, risks, and the path forward for the fourth industrial revolution

Hossin, Md Altab, Yin, Songtao, Dan, Ruibo and Chen, Lie (2025) Integrating artificial intelligence in unmanned vehicles: navigating uncertainties, risks, and the path forward for the fourth industrial revolution. Humanities and Social Sciences Communications, 12 (1). ISSN 2662-9992

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Abstract

Artificial intelligence (AI) has been a field of research for nearly 80 years. Relevant technologies and products have widely been used in our daily lives, scientific research, and military fields. AI is a technology that is likely to be the strongest driving force of the 4th industrial revolution. The widespread adoption of AI requires collaborative efforts across various sectors. However, frequent accidents involving unmanned vehicles (UVs) have exposed loopholes, uncertainties, and risks in the rapid development of AI. This study analyzes the assessment phases of AI for UVs, technological advancements, uncertainties, and risks and proposes strategies for moving forward. First, we extract the hidden patterns of uncertainty and risk associated with AI and then provide rational suggestions for the development of AI based on UVs by using natural language processing and data visualization techniques. Results show that real-time detection, processing, prediction, and decision for UVs are of utmost importance and need to be developed with the integration of AI for greater adoption. The assessment phases include detection, process, prediction, decision, and performance, each playing a crucial role in enhancing UV capabilities. However, despite rapid technological advancements in AI for UVs, uncertainties and risks persist, such as economic implications, ethical dilemmas, and safety concerns. Strategic roadmaps, collaboration among industry and regulators, public engagement initiatives, ethical AI frameworks, and innovation ecosystems are proposed as strategies to address current challenges and foster a sustainable integration of AI in UVs.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	LSE
Date Deposited:	06 Mar 2025 09:48
Last Modified:	06 Mar 2025 09:48
URI:	http://eprints.lse.ac.uk/id/eprint/127508

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