# Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios

arXiv: https://arxiv.org/abs/2305.14146 · HTML: https://mlsanity.com/paper/2305.14146/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2023.

## Built on by (most cited first, 8 of 8)

- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation](https://mlsanity.com/paper/2409.10320/) (2024)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Generative AI for Testing of Autonomous Driving Systems: A Survey](https://mlsanity.com/paper/2508.19882/) (2025)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions](https://mlsanity.com/paper/2512.11887/) (2025)
- [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)

## Abstract (arXiv)

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.