Self-driving car safety quantification via component-level analysis

TL;DR

This paper introduces a modular statistical method for assessing autonomous vehicle safety by analyzing individual components, demonstrated through an automated braking example to determine overall safety.

Contribution

It presents a novel component-level statistical framework for safety validation of autonomous vehicles, emphasizing the importance of sufficient and necessary conditions.

Findings

Component-level analysis can effectively evaluate vehicle safety.

Statistical methods help identify safety insufficiencies.

Cost benefits of modular safety assessment are demonstrated.

Abstract

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.