Waymo Safety Impact

The human benchmark data are the same as reported in Scanlon et al.

(2024), and extended upon in Kusano et al.

These benchmarks are derived from state police reported crash records and Vehicle Miles Traveled (VMT) data in the areas Waymo currently operates RO services at large scale (Phoenix, San Francisco, Los Angeles, and Austin).

The human benchmarks were made in a way that only included the crashes and VMT corresponding to passenger vehicles traveling on the types of roadways Waymo operates on (excluding freeways).

The any-injury-reported benchmark also used a 32% underreporting correction (based on NHTSA’s Blincoe et al., 2023 study to adjust for crashes not reported by humans.

The serious injury or worse (referred to as “suspected serious injury+” in the papers) and airbag deployment human benchmarks rates used the observed crashes without an underreporting correction.

All streets within a city are not equally challenging.

If Waymo drives more frequently in more challenging parts of the city that have higher crash rates, it may affect crash rates compared to quieter areas.

The benchmarks reported by Scanlon et al.

are at a city level, not for specific streets or areas.

The human benchmarks shown on this data hub were adjusted using a method described by Chen et al.

(2024) that models the effect of spatial distribution on crash risk.

The methodology adjusts the city-level benchmarks to account for the unique driving distribution of the Waymo driving.

The result of the reweighting method is human benchmarks that are more representative of the areas of the city Waymo drives in the most, which improves data alignment between the Waymo and human crash data.

Achieving the best possible data alignment, given the limitations of the available data, are part of the newly published Retrospective Automated Vehicle Evaluation (RAVE) best practices (Scanlon et al., 2024b).

This spatial dynamic benchmark approach described by Chen et al.

(2024) was also used in Kusano et al.