Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2023.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 4.
8 papers.
- 2024CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learningcited by 27
- 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Modelscited by 12
- 2024CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardeningcited by 2
- 2025Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safetycited by 1
- 2026Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shiftcited by 0
- 2026OmniSCS: Omni Safety-Critical Scenario Synthesis for Autonomous Driving via a Fully Editable Driving Worldcited by 0
- 2026When2Talk: When Should a Proactive In-Car Agent Talk?cited by 0
- 2026When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systemscited by 0
- 2026When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems12 Sep 2026
- 2026When2Talk: When Should a Proactive In-Car Agent Talk?11 Sep 2026
- 2026OmniSCS: Omni Safety-Critical Scenario Synthesis for Autonomous Driving via a Fully Editable Driving World7 Jul 2026
- 2026Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift16 Jun 2026
- 2025Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety27 Feb 2025
- 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models21 Feb 2025
- 2024CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening26 Nov 2024
- 2024CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning29 Mar 2024