# Ethical Decision Making During Automated Vehicle Crashes

arXiv: https://arxiv.org/abs/2010.16309 · HTML: https://mlsanity.com/paper/2010.16309/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [The Science Fiction Science Method](https://mlsanity.com/paper/2508.03430/) (2025)
- [Verifiable Obstacle Detection](https://mlsanity.com/paper/2208.14403/) (2022)
- [Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults](https://mlsanity.com/paper/2209.01710/) (2022)
- [Comprehensive systematic review into combinations of artificial intelligence, human factors, and automation](https://mlsanity.com/paper/2104.09233/) (2021)
- [Fairness Explainability using Optimal Transport with Applications in Image Classification](https://mlsanity.com/paper/2308.11090/) (2023)
- [EquiPy: Sequential Fairness using Optimal Transport in Python](https://mlsanity.com/paper/2503.09866/) (2025)

## Abstract (arXiv)

Automated vehicles have received much attention recently, particularly the DARPA Urban Challenge vehicles, Google's self-driving cars, and various others from auto manufacturers. These vehicles have the potential to significantly reduce crashes and improve roadway efficiency 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 pre-crash behavior. Unlike other automated vehicles--such as aircraft, where every collision is catastrophic, and guided track systems, which can only avoid collisions 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. This study investigates automated vehicle crashing and concludes the following: (1) automated vehicles will almost certainly crash, (2) an automated vehicle's decisions preceding certain crashes will have a moral component, and (3) there is no obvious way to effectively encode complex human morals in software. A three-phase approach to developing ethical crashing algorithms is presented, consisting 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.