mlsanityData to 7 Oct 2026

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

arXiv 2202.02215 ↗First posted 4 Feb 2022cs.ROcited by 100

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Cited by 37 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, the large population of cars, in general, leads to an extremely low collision rate, indicating that the safety-critical scenarios are rare in the collected real-world data. Thus, methods to artificially generate scenarios become crucial to measure the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving. We first provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -- fidelity, efficiency, diversity, transferability, controllability -- and research opportunities lighted up by these challenges.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 14; 2024: 22; 2025: 35; 2026: 28.

100 papers.

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  1. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  2. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
  3. 2023SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversariescited by 36
  4. 2023Empowering Autonomous Driving with Large Language Models: A Safety Perspectivecited by 34
  5. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  6. 2024RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Drivingcited by 30
  7. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  8. 2024Causal Composition Diffusion Model for Closed-loop Traffic Generationcited by 22
  9. 2024AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusioncited by 20
  10. 2024Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generationcited by 19
  11. 2023CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementaritycited by 18
  12. 2026Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failurescited by 17
38 more
  1. 2023Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Drivingcited by 16
  2. 2025LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Modelscited by 16
  3. 2023Dynamically Conservative Self-Driving Planner for Long-Tail Casescited by 13
  4. 2024FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversarialitycited by 13
  5. 2025Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learningcited by 12
  6. 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Modelscited by 11
  7. 2024UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarioscited by 10
  8. 2024A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challengescited by 10
  9. 2024SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generationcited by 10
  10. 2025LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarioscited by 9
  11. 2024Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Drivingcited by 8
  12. 2025From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarioscited by 8
  13. 2023A Scenario-Based Functional Testing Approach to Improving DNN Performancecited by 7
  14. 2025DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Modelscited by 7
  15. 2025Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Modelscited by 7
  16. 2023From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Drivingcited by 6
  17. 2023Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modelingcited by 5
  18. 2025AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Frameworkcited by 5
  19. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  20. 2024DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Timecited by 4
  21. 2025RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulationcited by 4
  22. 2025AgentSGEN: Multi-Agent LLM in the Loop for Semantic Collaboration and GENeration of Synthetic Datacited by 4
  23. 2023Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Drivingcited by 3
  24. 2024Scenario-Based Curriculum Generation for Multi-Agent Autonomous Drivingcited by 3
  25. 2024Feasibility Consistent Representation Learning for Safe Reinforcement Learningcited by 3
  26. 2024Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environmentscited by 3
  27. 2025HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generationcited by 3
  28. 2025Safety-Critical Traffic Simulation with Guided Latent Diffusion Modelcited by 3
  29. 2025Generative AI for Autonomous Driving: A Reviewcited by 3
  30. 2025RealDrive: Retrieval-Augmented Driving with Diffusion Modelscited by 3
  31. 2025AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Modelscited by 3
  32. 2025Generative AI for Testing of Autonomous Driving Systems: A Surveycited by 3
  33. 2025Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AIcited by 3
  34. 2025Controllable Collision Scenario Generation via Collision Pattern Predictioncited by 3
  35. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
  36. 2026CounterScene: Counterfactual Causal Reasoning in Generative World Models for Safety-Critical Closed-Loop Evaluationcited by 3
  37. 2024ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generationcited by 2
  38. 2024Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzingcited by 2

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