Veljko Dubljevic
North Carolina State University, NC·Social Science·Early-career
Self-driving cars sometimes face situations where any available action causes some harm, which means the software has to encode a stance on what counts as acceptable. Most work on this problem, both in algorithms and in the public surveys used to justify them, has assumed a utilitarian rule: minimize total harm.
- Fields
Social Science
Psychology
Computer Science
- Topics
autonomous vehicles
self-driving cars
machine ethics
- Grants
- 1 known grant, through Mar 2027
What they work on
- Develop new moral scenarios about potentially harmful traffic decisions involving vehicles and pedestrians, and render them as animations in an immersive virtual reality setting for controlled testing of moral judgment.
- Test the Agent-Deed-Consequence model, which decomposes moral judgment into intuitive evaluations of the agent's intentions, the deed, and the consequences at high or low stakes, against judgments collected from high school and college students.
- Compare moral judgments of professional moral philosophers with those of lay people who have no formal training in ethics, to determine how expertise shapes evaluations of traffic decisions.
- Build a prototype ethical algorithm based on the Agent-Deed-Consequence model and test how it interacts with human decision makers in a high-fidelity traffic simulator.
More about the research
But that framing has methodological problems and the general public pushes back strongly against it, so cars programmed that way may not match how people actually judge driving decisions. This project asks how human moral judgment in traffic really works, and builds and tests an alternative that reflects it.
Why it matters
Autonomous vehicles are being deployed while the ethical rules governing their behavior in unavoidable-harm situations remain unsettled and poorly matched to public intuitions. Evidence on how people actually judge driving decisions, and a working algorithm built from it, could inform how manufacturers, transportation engineers, and regulators specify vehicle behavior, and could support public engagement with how these systems decide.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Marcelo d'Amorim
North Carolina State University, NC·Computer Science
Improve the efficiency of simulation-based testing of autonomous driving systems by using risk signals from anomaly detectors during simulations to find infractions within a fixed time budget.
Deng Cao
Central State University, OH·Engineering
Design a coordinated strategy for connected self-driving vehicles to decide when to send perception tasks to roadside edge computers, keeping the decisions manageable as vehicle numbers grow and robust to uncertain edge behavior.
Xuegang Ban
University of Washington, WA·Engineering
Adapt and scale privacy-preserving techniques so that traffic, trajectory, and naturalistic driving data can be shared securely under both centralized and distributed data-sharing models, and evaluate them on real-world transportation datasets.
The school
North Carolina State University: 40 labs on PI Pages, $717M a year on research, 603 research doctorates a year.
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