Self-driving car safety quantification via component-level analysis
This addresses safety quantification for autonomous vehicles, but it appears incremental as it builds on existing modular analysis methods with a specific illustrative example.
The paper tackles the problem of quantifying self-driving car safety by proposing a modular statistical approach that analyzes constituent components, using an automated braking example to demonstrate how component-level performance studies can prove or disprove overall vehicle safety.
In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how separate perception system and operational design domain statistical analyses can be used to prove or disprove safety at the vehicle level.