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Waymo's Safety Methodologies and Safety Readiness Determinations

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arxiv 2011.00054 v1 pith:GQIRSG2S submitted 2020-10-30 cs.RO

classification cs.RO
keywords waymosafetymethodologiesreadinessspecificdeterminationdeterminationsengineering
verification ladder T0 review T1 audit T2 compute T3 formal
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Waymo's safety methodologies, which draw on well established engineering processes and address new safety challenges specific to Automated Vehicle technology, provide a firm foundation for safe deployment of Waymo's Level 4 ADS, which Waymo also refers to as the Waymo Driver. Waymo's determination of its readiness to deploy its AVs safely in different settings rests on that firm foundation and on a thorough analysis of risks specific to a particular Operational Design Domain. Waymo's process for making these readiness determinations entails an ordered examination of the relevant outputs from all of its safety methodologies combined with careful safety and engineering judgment focused on the specific facts relevant for a particular determination. Waymo will approve when it determines the ADS is ready for the new conditions without creating any unreasonable risks to safety. This paper explains Waymo's methodologies as applied to the three layers of its technology: hardware, ADS behavior, and operations, and also explains Waymo's safety governance. Waymo will continue to apply and adapt those methodologies, and to learn from the important contributions of others in the AV industry, as Waymo continues to build an ever safer and more able ADS.

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Cited by 3 Pith papers

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

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    cs.RO 2025-07 conditional novelty 6.0 of 10

    RCG replaces handcrafted adversarial scenario scoring with a crash-grounded embedding and k-NN selection, yielding a 9.2% average relative improvement in ego success.

  2. On the Practices of Autonomous Systems Development: Survey-based Empirical Findings

    cs.SE 2025-06 conditional novelty 6.0 of 10

    A 2019 survey of 110 experts shows autonomous systems practice relied mostly on rule-based methods, high autonomy levels, and relatively little machine learning, with weak adoption of modeling standards and certification.

  3. The DevSafeOps Dilemma: A Systematic Literature Review on Rapidity in Safe Autonomous Driving Development and Operation

    cs.SE 2025-06 conditional novelty 5.0 of 10

    A systematic review maps 11 clusters of challenges and their proposed solutions for applying DevOps to safe autonomous driving development.

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