%0 Journal Article %1 9763411 %A Zhang, Xinhai %A Tao, Jianbo %A Tan, Kaige %A Törngren, Martin %A Sánchez, José Manuel Gaspar %A Ramli, Muhammad Rusyadi %A Tao, Xin %A Gyllenhammar, Magnus %A Wotawa, Franz %A Mohan, Naveen %A Nica, Mihai %A Felbinger, Hermann %D 2023 %J IEEE Transactions on Software Engineering %K SLR %N 3 %P 991-1026 %R 10.1109/TSE.2022.3170122 %T Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study %V 49 %X 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. 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.

@article{9763411, 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 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. 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.}, added-at = {2023-08-09T19:02:13.000+0200}, author = {Zhang, Xinhai and Tao, Jianbo and Tan, Kaige and Törngren, Martin and Sánchez, José Manuel Gaspar and Ramli, Muhammad Rusyadi and Tao, Xin and Gyllenhammar, Magnus and Wotawa, Franz and Mohan, Naveen and Nica, Mihai and Felbinger, Hermann}, biburl = {https://www.bibsonomy.org/bibtex/2428fbf46a05d3bf7df9d2dcc1d5dd04f/gabrielkenji}, doi = {10.1109/TSE.2022.3170122}, interhash = {2f79bc5e9e4a54cf252d4ac3472109dd}, intrahash = {428fbf46a05d3bf7df9d2dcc1d5dd04f}, issn = {1939-3520}, journal = {IEEE Transactions on Software Engineering}, keywords = {SLR}, month = {March}, number = 3, pages = {991-1026}, timestamp = {2023-08-09T19:02:13.000+0200}, title = {Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study}, volume = 49, year = 2023 }