Where ADS Crashes Differ: A Harmonized Comparison Against Human Baselines
Crash-rate comparisons between automated driving systems (ADS) and human drivers require an exposure denominator that only a few operators publish. We instead ask a composition question: given a crash, does the mix of crash types differ between ADS and human-driven vehicles? The question needs no mileage and can therefore cover every ADS operator. We assemble the NHTSA Standing General Order (SGO) ADS incident files across both file generations into 2,450 distinct in-scope incidents (2021-2026), of which 828 fall within the 2021-2024 window covered by the Crash Report Sampling System (CRSS), the national police-reported human comparator. A schema-constrained large language model (LLM) codes each SGO narrative into the CRSS crash-type taxonomy. Because SGO compels reporting far below the police-reporting threshold, we bracket reportability with an objective severity floor and a reasonable-person rubric, and claim a difference only where it survives both. Two composition differences hold under every reportability definition, tow definition and weighting of the human side, and under both definitions of driverless operation: turn-across-path crashes are under-represented among ADS crashes (0.23-0.46x the CRSS share under the primary driverless definition) and so are single-vehicle crashes (0.19-0.56x). In severity-floor rear-end crashes the ADS vehicle is the striking party at a quasi-induced-exposure ratio of 0.121 (95% CI [0.05, 0.23]) against a human baseline of 1.000. If the ADS vehicle is also struck more often than its exposure implies, this ratio overstates the ADS-human difference. We also document three pitfalls in the federal data: an operator-specific coding of driverless operation, a tow-away criterion confounded by fleet recovery practice, and an occupancy confound that accounts for about 42% of the apparent ADS-human severity gap.