mlsanityData to 14 Oct 2026

Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios

arXiv 2004.06531 ↗First posted 14 Apr 2020cs.RO cs.LG stat.MLcited by 43

Not on the map. Why?

Cited by 22 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in generating challenging scenarios for tested vehicles. In this paper, we propose an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning. Considering the multimodal nature of dangerous scenarios, we use ensemble models to represent different local optimums for diversity. We then utilize a nonparametric Bayesian method to cluster the adversarial policies. The proposed method is validated in a typical lane-change scenario that involves frequent interactions between the ego vehicle and the surrounding vehicles. Results show that the adversarial scenarios generated by our method significantly degrade the performance of the tested vehicles. We also illustrate different patterns of generated adversarial environments, which can be used to infer the weaknesses of the tested vehicles.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 12; 2024: 9; 2025: 9; 2026: 4.

43 papers.

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  1. 2022Guided Conditional Diffusion for Controllable Traffic Simulationcited by 190
  2. 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 139
  3. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 137
  4. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehiclescited by 93
  5. 2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
  6. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  7. 2022TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Drivingcited by 23
  8. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  9. 2023Reinforcement Learning with Human Feedback for Realistic Traffic Simulationcited by 21
  10. 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
  11. 2024Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generationcited by 19
  12. 2025Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Testcited by 18
31 more
  1. 2021Diverse Critical Interaction Generation for Planning and Planner Evaluationcited by 15
  2. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priorscited by 15
  3. 2024Learning to Drive via Asymmetric Self-Playcited by 11
  4. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  5. 2021Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systemscited by 8
  6. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  7. 2023EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanismcited by 6
  8. 2023Robust Driving Policy Learning with Guided Meta Reinforcement Learningcited by 5
  9. 2025Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehiclescited by 5
  10. 2023CC-SGG: Corner Case Scenario Generation using Learned Scene Graphscited by 4
  11. 2023DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Datacited by 3
  12. 2024GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learningcited by 3
  13. 2024Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environmentscited by 3
  14. 2024Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Reviewcited by 3
  15. 2022ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Drivingcited by 2
  16. 2025AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Modelscited by 2
  17. 2025From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Drivingcited by 2
  18. 2024VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustnesscited by 1
  19. 2024LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantizationcited by 1
  20. 2025AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Testscited by 1
  21. 2025Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safetycited by 1
  22. 2025Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarioscited by 1
  23. 2025Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Drivingcited by 1
  24. 2023Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flowcited by 0
  25. 2023Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehiclescited by 0
  26. 2023Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluationcited by 0
  27. 2025Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenariocited by 0
  28. 2026Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generationcited by 0
  29. 2026ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Drivingcited by 0
  30. 2026End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generationcited by 0
  31. 2026Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generationcited by 0