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Ethical Decision Making during Automated Vehicle Crashes
Open accessAbstract
Automated vehicles have received much attention recently, particularly the Defense Advanced Research Projects Agency Urban Challenge vehicles, Google's self-driving cars, and various others from auto manufacturers. These vehicles have the potential to reduce crashes and improve roadway efficiency significantly by automating the responsibilities of the driver. Still, automated vehicles are expected to crash occasionally, even when all sensors, vehicle control components, and algorithms function perfectly. If a human driver is unable to take control in time, a computer will be responsible for precrash behavior. Unlike other automated vehicles, such as aircraft, in which every collision is catastrophic, and unlike guided track systems, which can avoid collisions only in one dimension, automated roadway vehicles can predict various crash trajectory alternatives and select a path with the lowest damage or likelihood of collision. In some situations, the preferred path may be ambiguous. The study reported here investigated automated vehicle crashing and concluded the following: (a) automated vehicles would almost certainly crash, (b) an automated vehicle's decisions that preceded certain crashes had a moral component, and (c) there was no obvious way to encode complex human morals effectively in software. The paper presents a three-phase approach to develop ethical crashing algorithms; the approach consists of a rational approach, an artificial intelligence approach, and a natural language requirement. The phases are theoretical and should be implemented as the technology becomes available.
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“Contrasting and incommensurable dimensions are likely to emerge (Goodall, 2014) when designing an algorithm to reduce the harm of a given crash.”
From Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020) · cites this paper as Goodall, 2014
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Metadata
- Authors
- Goodall, N. J.
- Affiliations
- Virginia Transportation Research Council (US)
- Corresponding author
- Goodall, Noah J.
- ORCID
- Goodall, Noah J.
- Venue
- Transportation Research Record: Journal of the Transportation Research Board
- Publisher
- SAGE Publications
- Published
- 2014-01
- Volume
- 2424
- Issue
- 1
- Pages
- 58-65
- ISSN
- 0361-1981 (ISSN-L)
- 2169-4052
- OpenAlex
- W2118960450
- Cited by
- 292
- References
- 38
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 18 · 2023: 28 · 2022: 28 · 2021: 32 · 2020: 41 · 2019: 28 · 2018: 35 · 2017: 26 · 2016: 18 · 2015: 8 · 2014: 2
- FWCI
- 22.04
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Transportation Research Record Journal of the Transportation Research Board (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- University of Virginia
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- autonomous vehicles, ethical decision-making, traffic accidents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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