Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Explainable AI for Transparent Decision-Making in Autonomous Vehicle Cybersecurity Operations

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  • Dr. Andrés Herrera
Dr. Andrés Herrera
Professor of Industrial Engineering, Universidad de los Andes (UNIANDES), Colombia
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Published 30-12-2022

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How to Cite

[1]
Dr. Andrés Herrera, “Explainable AI for Transparent Decision-Making in Autonomous Vehicle Cybersecurity Operations”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 31–52, Dec. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/77

Abstract

Introduction: We are proposing to conduct fundamental and basic research in Explainable Artificial Intelligence (XAI) for transparent decision-making for cyber-attack response strategy and tactics in autonomous vehicle (AV) cybersecurity operations, as AVs embody a promising domain where XAI promises amplified situation awareness and transparent and explainable decision-making that can be leveraged for application to both the AV and broader domains. The significance and impact of this project also extends to national priorities and national defense as the agencies of the Research, Technology and Development RDT&E community continue to face challenges to conduct cyber-operations in networked environments, and the success of these cyber-operations directly depends on the effectiveness, speed, and defensibility of the cyber-response processes, especially for sectors like transportation and critical infrastructure that draw significant attention from adversaries. This research will be conducted at West Virginia University (WVU) in conjunction with the institution's capabilities in data, data science, artificial intelligence and machine learning, human factors, and autonomous systems, in the Center for Advanced Communications Avionics-Undermanned Systems CAC-AUS and Transportation and Innovation Institute TII, that include a focus on autonomous transportation systems.

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