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

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arxiv 2306.01917 v1 pith:HBGO65GD submitted 2023-06-02 cs.CY

classification cs.CY
keywords safetyapproachcasecrediblebuildingdeterminationsystemwaymo
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Assessing a Safety Case: Bottom-up Guidance for Claims and Evidence Evaluation

    cs.SE 2025-06 accept novelty 6.0 of 10

    The paper defines scoring rubrics for claim support (procedural and implementation) and evidence status, forming a bottom-up method for ADS safety case credibility assessment.

  2. Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey

    cs.RO 2025-09 conditional novelty 4.0 of 10

    A structured survey of LLM-based trajectory prediction methods, organized into trajectory-language mapping, multimodal fusion, and constraint-based reasoning, with benchmarks, metrics, and future directions.

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