Positive Trust Balance for Self-Driving Car Deployment
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Abstract (arXiv)
The crucial decision about when self-driving cars are ready to deploy is likely to be made with insufficient lagging metric data to provide high confidence in an acceptable safety outcome. A Positive Trust Balance approach can help with making a responsible deployment decision despite this uncertainty. With this approach, a reasonable initial expectation of safety is based on a combination of a practicable amount of testing, engineering rigor, safety culture, and a strong commitment to use post-deployment operational feedback to further reduce uncertainty. This can enable faster deployment than would be required by more traditional safety approaches by reducing the confidence necessary at time of deployment in exchange for a more stringent requirement for Safety Performance Indicator (SPI) field feedback in the context of a strong safety culture.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2023Safety Performance of Neural Networks in the Presence of Covariate Shiftcited by 1
- 2024The Open Autonomy Safety Case Frameworkcited by 1
- 2022Safety-Critical Adaptation in Self-Adaptive Systemscited by 0
- 2024The Open Autonomy Safety Case Framework8 Apr 2024
- 2023Safety Performance of Neural Networks in the Presence of Covariate Shift24 Jul 2023
- 2022Safety-Critical Adaptation in Self-Adaptive Systems30 Sep 2022