Benchmarks for Retrospective Automated Driving System Crash Rate Analysis Using Police-Reported Crash Data

TL;DR

This paper develops transparent, repeatable benchmarks for comparing automated driving system crash rates to human driver crash data using police reports, addressing data challenges and controlling for key variables.

Contribution

It introduces a methodology for generating comparable crash benchmarks from police data, including underreporting correction and controlling for geographic and vehicle factors.

Findings

Underreporting correction improves comparability of crash data.

Controlling for region, road type, and vehicle type reduces bias.

Publicly accessible data enables transparent benchmark creation.

Abstract

With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the US, we are now approaching an inflection point, where the process of retrospectively evaluating ADS safety impact can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a "benchmark" crash rate. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identify important dependencies in controlling for geographic region, road type, and vehicle type, and show how failing to control for these features can bias results. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.