Modeling Ethical and Operational Preferences in Automated Driving Systems

William N. Caballero (), Roi Naveiro () and David Ríos Insua ()
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William N. Caballero: United States Air Force Academy, Colorado Springs, Colorado 80840
Roi Naveiro: Institute of Mathematical Sciences, 28049 Madrid, Spain
David Ríos Insua: Institute of Mathematical Sciences, 28049 Madrid, Spain

Decision Analysis, 2022, vol. 19, issue 1, 21-43

Abstract: Whereas automated driving technology has made tremendous gains in the last decade, significant questions remain regarding its integration into society. Given its revolutionary nature, the use of automated driving systems (ADSs) is accompanied by myriad novel quandaries relating to both operational and ethical concerns that are relevant to numerous stakeholders (e.g., governments, manufacturers, and passengers). When considering any such problem, the ADS’s decision-making calculus is always a central component. This is true for concerns about public perception and trust to others regarding explainability and legal certainty. Therefore, in this manuscript, we set forth a general decision-analytic framework tailorable to multitudinous stakeholders. More specifically, we develop and validate a generic tree of ADS management objectives, explore potential attributes for their measurement, and provide multiattribute utility functions for implementation. Given the contention surrounding numerous ethical concerns in ADS operations, we explore how each of the aforementioned components can be tailored in accordance with the stakeholder’s desired ethical perspective. A simulation environment is developed upon which our framework is tested. Within this environment we illustrate how our approach can be leveraged by stakeholders to make strategic trade-offs regarding ADS behavior and to inform policymaking efforts. In so doing, our framework is demonstrated as a practical, tractable, and transparent means of modeling ADS decision making.

Keywords: automated driving systems; ethics; multicriteria decision analysis; self-driving vehicles; explainability (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:19:y:2022:i:1:p:21-43

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