Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

TL;DR

This systematic literature review analyzes methods for identifying critical scenarios in autonomous driving, proposing a comprehensive taxonomy and highlighting open research challenges to improve safety validation.

Contribution

It introduces a detailed taxonomy for critical scenario identification and reviews 86 papers, outlining current research and future directions in autonomous driving safety.

Findings

Developed a taxonomy covering problem definition, methods, and assessment.

Reviewed 86 papers from 2017-2020 on critical scenario identification.

Identified open issues like coverage, practicability, and scenario space explosion.

Abstract

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.