# Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk

arXiv: https://arxiv.org/abs/2306.01917 · HTML: https://mlsanity.com/paper/2306.01917/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2023.

## Built on by (most cited first, 11 of 11)

- [Evaluating Gemini Robotics Policies in a Veo World Simulator](https://mlsanity.com/paper/2512.10675/) (2025)
- [Decoupled Diffusion Sparks Adaptive Scene Generation](https://mlsanity.com/paper/2504.10485/) (2025)
- [A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness](https://mlsanity.com/paper/2308.01050/) (2023)
- [Adaptive Neural Networks for Intelligent Data-Driven Development](https://mlsanity.com/paper/2502.10603/) (2025)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [The Open Autonomy Safety Case Framework](https://mlsanity.com/paper/2404.05444/) (2024)
- [Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove](https://mlsanity.com/paper/2609.10951/) (2026)
- [An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving](https://mlsanity.com/paper/2409.06607/) (2024)
- [A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences](https://mlsanity.com/paper/2507.18326/) (2025)
- [A Five-Layer MLOps Architecture for Connected Automated Driving](https://mlsanity.com/paper/2605.12719/) (2026)
- [Vernata: Self-Supervised Learning of LiDAR Point Representations](https://mlsanity.com/paper/2608.06919/) (2026)

## Abstract (arXiv)

This paper presents an overview of Waymo's approach to building a reliable case for safety - a novel and thorough blueprint for use by any company building fully autonomous driving systems. A safety case for fully autonomous operations is a formal way to explain how a company determines that an AV system is safe enough to be deployed on public roads without a human driver, and it includes evidence to support that determination. It involves an explanation of the system, the methodologies used to develop it, the metrics used to validate it and the actual results of validation tests. Yet, in order to develop a worthwhile safety case, it is first important to understand what makes one credible and well crafted, and align on evaluation criteria. This paper helps enabling such alignment by providing foundational thinking into not only how a system is determined to be ready for deployment but also into justifying that the set of acceptance criteria employed in such determination is sufficient and that their evaluation (and associated methods) is credible. The publication is structured around three complementary perspectives on safety that build upon content published by Waymo since 2020: a layered approach to safety; a dynamic approach to safety; and a credible approach to safety. The proposed approach is methodology-agnostic, so that anyone in the space could employ portions or all of it.