After several years of public road testing, the commercial deployment of fully autonomous vehicles-or Automated Driving Systems (ADS)-is poised to scale substantially following significant technological advancements and recent regulatory approvals. However, the fundamental question of whether an ADS is safer than its human counterparts remain largely unsolved due to several challenges in establishing an appropriate real-world safety comparison method. As scaling ensues, the lack of an established method can contribute to misinterpretations or uncertainties regarding ADS safety and impede the continuous and consistent assessment of ADS performance. This study introduces three research developments to define a robust and replicable safety comparison method to address this critical methodological gap. First, we introduce the use of liability insurance claims data to measure the comparative safety between ADS and human drivers. Second, we use Swiss Re insurance claims data to establish the first zip code- and responsibility-calibrated human performance benchmark, composed of over 600,000 private passenger vehicle claims and 125 billion miles of driving exposure. Third, we perform a case study by applying the developed baseline to evaluate the safety impact of the Waymo Driver. We find that when benchmarked against zip code-calibrated human baselines, the Waymo Driver significantly improves safety towards other road users. The comparison method established in this study can be replicated for other regions or ADS deployments to aid the decision-making of ADS safety stakeholders such as regulators, and instill trust in the general public.
© 2024 Published by Elsevier Ltd.