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

Abstract

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.

Accepted Manuscript for publication in Traffic Injury Prevention

References in corpus (3)

• Benchmarks for Retrospective Automated Driving System Crash Rate Analysis Using Police-Reported Crash Data
• Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk
• Interpreting Safety Outcomes: Waymo's Performance Evaluation in the Context of a Broader Determination of Safety Readiness

Cited by in corpus (5)

• Benchmarks for Retrospective Automated Driving System Crash Rate Analysis Using Police-Reported Crash Data
• Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI
• Validation of human benchmark models for Automated Driving System approval: How competent and careful are they really?
• RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems
• Comparability of Automated Vehicle Crash Databases