Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

TL;DR

This paper reviews criticality metrics used in automated driving to assess traffic risks and safety, proposing a systematic suitability analysis method to help practitioners select appropriate metrics for different applications.

Contribution

It provides an extensive review of criticality metrics, their properties, and applications, and introduces a systematic suitability analysis method for selecting appropriate metrics in automated driving.

Findings

Criticality metrics are vital for assessing traffic conflicts and safety in automated driving.

The proposed suitability analysis aids practitioners in choosing appropriate metrics.

The review highlights gaps and future directions in criticality metric research.

Abstract

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness.