Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Xinhai Zhang, Jianbo Tao, Kaige Tan, Martin Torngren, Jose Manuel Gaspar Sanchez, Muhammad Rusyadi Ramli, Xin Tao, Magnus Gyllenhammar, Franz Wotawa, Naveen Mohan, Mihai Nica, Hermann Felbinger

Research output: Contribution to journalArticlepeer-review


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 Automated Driving System or Advanced Driving-Assistance System 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 mapping study 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.

Original languageEnglish
Pages (from-to)991-1026
JournalIEEE Transactions on Software Engineering
Issue number3
Publication statusPublished - 2023


  • Automated Driving
  • Bibliographies
  • Complexity theory
  • Critical Scenario
  • Roads
  • Systematic Mapping Study
  • Systematics
  • Taxonomy
  • Terminology
  • Uncertainty

ASJC Scopus subject areas

  • Software

Fields of Expertise

  • Information, Communication & Computing


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