Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk
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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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jun 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 3.
11 papers.
- 2025Evaluating Gemini Robotics Policies in a Veo World Simulatorcited by 51
- 2025Decoupled Diffusion Sparks Adaptive Scene Generationcited by 12
- 2023A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskinesscited by 3
- 2025Adaptive Neural Networks for Intelligent Data-Driven Developmentcited by 3
- 2025Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Surveycited by 3
- 2024The Open Autonomy Safety Case Frameworkcited by 1
- 2026Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drovecited by 1
- 2024An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Drivingcited by 0
- 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differencescited by 0
- 2026A Five-Layer MLOps Architecture for Connected Automated Drivingcited by 0
- 2026Vernata: Self-Supervised Learning of LiDAR Point Representationscited by 0
- 2026Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove10 Sep 2026
- 2026Vernata: Self-Supervised Learning of LiDAR Point Representations7 Aug 2026
- 2026A Five-Layer MLOps Architecture for Connected Automated Driving12 May 2026
- 2025Evaluating Gemini Robotics Policies in a Veo World Simulator11 Dec 2025
- 2025Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey11 Sep 2025
- 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences24 Jul 2025
- 2025Decoupled Diffusion Sparks Adaptive Scene Generation14 Apr 2025
- 2025Adaptive Neural Networks for Intelligent Data-Driven Development14 Feb 2025
- 2024An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving10 Sep 2024
- 2024The Open Autonomy Safety Case Framework8 Apr 2024
- 2023A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness2 Aug 2023