# The Open Autonomy Safety Case Framework

arXiv: https://arxiv.org/abs/2404.05444 · HTML: https://mlsanity.com/paper/2404.05444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk](https://mlsanity.com/paper/2306.01917/) (2023)
- [Positive Trust Balance for Self-Driving Car Deployment](https://mlsanity.com/paper/2009.05801/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Five-Layer MLOps Architecture for Connected Automated Driving](https://mlsanity.com/paper/2605.12719/) (2026)

## Abstract (arXiv)

A system safety case is a compelling, comprehensible, and valid argument about the satisfaction of the safety goals of a given system operating in a given environment supported by convincing evidence. Since the publication of UL 4600 in 2020, safety cases have become a best practice for measuring, managing, and communicating the safety of autonomous vehicles (AVs). Although UL 4600 provides guidance on how to build the safety case for an AV, the complexity of AVs and their operating environments, the novelty of the used technology, the need for complying with various regulations and technical standards, and for addressing cybersecurity concerns and ethical considerations make the development of safety cases for AVs challenging. To this end, safety case frameworks have been proposed that bring strategies, argument templates, and other guidance together to support the development of a safety case. This paper introduces the Open Autonomy Safety Case Framework, developed over years of work with the autonomous vehicle industry, as a roadmap for how AVs can be deployed safely and responsibly.