# Self-driving car safety quantification via component-level analysis

arXiv: https://arxiv.org/abs/2009.01119 · HTML: https://mlsanity.com/paper/2009.01119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [The missing link: Developing a safety case for perception components in automated driving](https://mlsanity.com/paper/2108.13294/) (2021)
- [STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation](https://mlsanity.com/paper/2312.09559/) (2023)

## Abstract (arXiv)

In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how separate perception system and operational design domain statistical analyses can be used to prove or disprove safety at the vehicle level.