1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey
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Abstract (arXiv)
Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and autonomous driving systems. However, the term scenario generation is used for many different methods, e.g., extraction of scenarios from naturalistic driving data or variation of scenario parameters. This survey aims to give a systematic overview of different approaches, establish different categories of scenario acquisition and generation, and show that each group of methods has typical input and output types. It shows that although the term is often used throughout literature, the evaluated methods use different inputs and the resulting scenarios differ in abstraction level and from a systematical point of view. Additionally, recent research and literature examples are given to underline this categorization.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2023.
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Citing papers per year: 2024: 2; 2025: 5; 2026: 1.
8 papers.
- 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
- 2025Controllable Collision Scenario Generation via Collision Pattern Predictioncited by 3
- 2024Rate-Informed Discovery via Bayesian Adaptive Multifidelity Samplingcited by 2
- 2025Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarioscited by 1
- 2025Why Braking? Scenario Extraction and Reasoning Utilizing LLMcited by 1
- 2026Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Drivingcited by 1
- 2024GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulationcited by 0
- 2025Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systemscited by 0
- 2026Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving15 Jul 2026
- 2025Controllable Collision Scenario Generation via Collision Pattern Prediction14 Oct 2025
- 2025Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems30 Jul 2025
- 2025Why Braking? Scenario Extraction and Reasoning Utilizing LLM17 Jul 2025
- 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis13 Jun 2025
- 2025Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios31 Mar 2025
- 2024Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling26 Nov 2024
- 2024GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation17 Oct 2024