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. (2025).
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