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Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles

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arxiv 2312.12675 v3 pith:GCAOE6KI submitted 2023-12-20 cs.RO

Kristofer D. Kusano , John M. Scanlon , Yin-Hsiu Chen , Timothy L. McMurry , Ruoshu Chen , Tilia Gode , Trent Victor This is my paper · ORCID
classification cs.RO
keywords humanratecrashipmmtogethervehicleany-injury-reportedbenchmark
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This paper examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. ADS crash data was derived from NHTSA's Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, AZ, San Francisco, CA, and Los Angeles, CA. When considering all locations together, the any-injury-reported crashed vehicle rate was 0.6 incidents per million miles (IPMM) for the ADS vs 2.80 IPMM for the human benchmark, an 80% reduction or a human crash rate that is 5 times higher than the ADS rate. Police-reported crashed vehicle rates for all locations together were 2.1 IPMM for the ADS vs. 4.68 IPMM for the human benchmark, a 55% reduction or a human crash rate that was 2.2 times higher than the ADS rate. Police-reported and any-injury-reported crashed vehicle rate reductions for the ADS were statistically significant when compared in San Francisco and Phoenix, as well as combined across all locations (except for any-injury-reported in Phoenix). The any property damage or injury comparison had statistically significant decrease in 3 comparisons, but also non-significant results in 3 other benchmarks. Given imprecision in the benchmark estimate and multiple potential sources of underreporting biasing the benchmarks, caution should be taken when interpreting the results of the any property damage or injury comparison. Together, these crash-rate results should be interpreted as a directional and continuous confidence growth indicator, together with other methodologies, in a safety case approach.

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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. Determining Absence of Unreasonable Risk: Approval Guidelines for an Automated Driving System Deployment

    cs.SE 2025-05 unverdicted novelty 5.0 of 10

    Twelve acceptance criteria spanning system safety, testing, risk management, and field monitoring form a proposed framework for approving automated driving system deployments.

  2. CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening

    cs.LG 2024-11 conditional novelty 4.0 of 10

    An adversarial RL framework that both finds collision-inducing scenarios and retrains a motion planner against them, cutting crash rates in a two-vehicle highway simulator.

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