mlsanityData to 7 Oct 2026

Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

arXiv 2003.01197 ↗First posted 2 Mar 2020cs.RO cs.LGcited by 74

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Cited by 36 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Long-tail and rare event problems become crucial when autonomous driving algorithms are applied in the real world. For the purpose of evaluating systems in challenging settings, we propose a generative framework to create safety-critical scenarios for evaluating specific task algorithms. We first represent the traffic scenarios with a series of autoregressive building blocks and generate diverse scenarios by sampling from the joint distribution of these blocks. We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated. We regard the task algorithm as an environment (or a discriminator) that returns a reward to the agent when a risky scenario is generated. Through the experiments conducted on several scenarios in the simulation, we demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods. Another advantage of this method is its adaptiveness to the routes and parameters.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 9; 2023: 16; 2024: 11; 2025: 17; 2026: 10.

74 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 136
  3. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
  4. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  5. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehiclescited by 92
  6. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  7. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  8. 2022AdvDO: Realistic Adversarial Attacks for Trajectory Predictioncited by 48
  9. 2023Scenario Diffusion: Controllable Driving Scenario Generation With Diffusioncited by 46
  10. 2023RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarioscited by 46
  11. 2024Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?cited by 38
  12. 2023SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversariescited by 36
38 more
  1. 2021Context-Aware Safe Reinforcement Learning for Non-Stationary Environmentscited by 33
  2. 2021Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehiclescited by 27
  3. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  4. 2021CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generationcited by 20
  5. 2024AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusioncited by 20
  6. 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
  7. 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Drivingcited by 17
  8. 2025LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Modelscited by 16
  9. 2025Challenger: Affordable Adversarial Driving Video Generationcited by 16
  10. 2021Diverse Critical Interaction Generation for Planning and Planner Evaluationcited by 15
  11. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priorscited by 15
  12. 2024Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Modelcited by 14
  13. 2024FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversarialitycited by 13
  14. 2025ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectorycited by 12
  15. 2024Learning to Drive via Asymmetric Self-Playcited by 11
  16. 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Modelscited by 11
  17. 2023Learning to View: Decision Transformers for Active Object Detectioncited by 10
  18. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  19. 2024SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generationcited by 10
  20. 2025LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarioscited by 9
  21. 2021Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systemscited by 8
  22. 2022ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLAcited by 8
  23. 2022RITA: Boost Driving Simulators with Realistic Interactive Traffic Flowcited by 7
  24. 2025Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Modelscited by 7
  25. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  26. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledgecited by 4
  27. 2022Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systemscited by 4
  28. 2023CC-SGG: Corner Case Scenario Generation using Learned Scene Graphscited by 4
  29. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  30. 2025On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehiclescited by 4
  31. 2025Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehiclescited by 4
  32. 2023Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Drivingcited by 3
  33. 2025SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integrationcited by 3
  34. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
  35. 2022ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Drivingcited by 2
  36. 2025AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reportscited by 2
  37. 2021Semantically Adversarial Scenario Generation with Explicit Knowledge Guidancecited by 1
  38. 2022Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Samplingcited by 1

50 most cited of 74 All 74 (JSON)