Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios

Qunying Song, Emelie Engström, Per Runeson

Testing autonomous driving systems for safety and reliability is extremely complex. A primary challenge is identifying the relevant test scenarios, especially the critical ones that may expose hazards or risks of harm to autonomous vehicles and other road users. There are several proposed methods and tools for critical scenario identification, while the industry practices, such as the selection, implementation, and limitations of the approaches, are not well understood. In this study, we conducted 10 interviews with 13 interviewees from 7 companies in autonomous driving in Sweden. We used thematic modeling to analyse and synthesize the interview data. We found there are little joint efforts in the industry to explore different approaches and tools, and every approach has its own limitations and weaknesses. To that end, we recommend combining different approaches available, collaborating among different stakeholders, and continuously learning the field of critical scenario identification and testing. The contributions of our study are the exploration and synthesis of the industry practices and related challenges for critical scenario identification and testing, and the potential increase of the industry relevance for future studies in related topics.

Knowledge Graph



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