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

AI-Powered Ethical Decision-Making Frameworks for Autonomous Vehicles

Dr. Pascal Fua
Professor of Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Cover

Published 24-06-2024

Keywords

  • automated vehicles,
  • (AVs)

How to Cite

[1]
Dr. Pascal Fua, “AI-Powered Ethical Decision-Making Frameworks for Autonomous Vehicles”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 1–22, Jun. 2024, Accessed: Nov. 23, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/56

Abstract

The distinguishing features of this research with respect to the existing literature and the stated objectives include the development of multi-tier decision-making criteria of AV that are adaptable in nature and context-aware with respect to moral, legal, technological, and situational considerations, and operationalize the framework based on a deep-learning-based model of AV decision-making to demonstrate the feasibility and effectiveness of the proposed framework in real-time traffic conditions [1]. In order to realize the objective, this research hosts three significant outcomes. Firstly, the individual and contextual factors that need to be addressed on ethical AV decision-making from all the related fields will be articulated. Secondly, an ethical decision-making framework that allows a road-field user of non-ethical AV extend further, based on the ethical consideration of various state-of-the-art ethical decision-making frameworks will be suggested. Frailties of one might be mitigated by operationalizing another ethical decision-making approach and provisions may be made for fallback infrastructure for safe and better traffic conditions. Lastly, the multi-tier ethical decision-making criterion based on moral, legal, technological and situational considerations will be developed and then improvised with respect to the required scenario. Finally, an AV car-following model was developed using the above proposed ethical frameworks and parameters were tuned to understand the behavior of the AV following these proposed models in typical stop and go scenario.

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