mlsanityData to 7 Oct 2026

AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

arXiv 2101.06549 ↗First posted 16 Jan 2021cs.RO cs.AI cs.CVcited by 132

Not on the map. Why?

Cited by 51 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 16 Jan 2021.

Builds on

36 of 56 references.

References · 36

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
  4. 2016End to End Learning for Self-Driving Carscited by 1,000
  5. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
  6. 2017Adversarial Patchcited by 514
  7. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  8. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  9. 2018Learning to Drive in a Daycited by 266
  10. 2019Simple Black-box Adversarial Attackscited by 249
  11. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  12. 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 151
24 more
  1. 2020Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representationscited by 150
  2. 2018Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priorscited by 145
  3. 2020Physically Realizable Adversarial Examples for LiDAR Object Detectioncited by 137
  4. 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
  5. 2020Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasurescited by 95
  6. 2018Driving Policy Transfer via Modularity and Abstractioncited by 91
  7. 2019Urban Driving with Conditional Imitation Learningcited by 84
  8. 2018GenAttack: Practical Black-box Attacks with Gradient-Free Optimizationcited by 82
  9. 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 76
  10. 2020Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Methodcited by 74
  11. 2017Adversarial Attacks Beyond the Image Spacecited by 67
  12. 2019Adversarial Objects Against LiDAR-Based Autonomous Driving Systemscited by 62
  13. 2020DSDNet: Deep Structured self-Driving Networkcited by 61
  14. 2020Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluationcited by 54
  15. 2019Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehiclescited by 51
  16. 2020Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarioscited by 42
  17. 2018Learning to Drive from Simulation without Real World Labelscited by 39
  18. 2020Physical Adversarial Attack on Vehicle Detector in the Carla Simulatorcited by 38
  19. 2019Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Drivingcited by 36
  20. 2020Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systemscited by 34
  21. 2019Efficient Black-box Assessment of Autonomous Vehicle Safetycited by 23
  22. 2019Adversarial T-shirt! Evading Person Detectors in A Physical Worldcited by 13
  23. 2020Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learningcited by 11
  24. 2020A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challengecited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 17; 2023: 31; 2024: 22; 2025: 32; 2026: 23.

132 papers.

Sort:

By area:

  1. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 527
  2. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  4. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 294
  5. 2022Guided Conditional Diffusion for Controllable Traffic Simulationcited by 189
  6. 2023Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusioncited by 137
  7. 2021Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Priorcited by 136
  8. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  9. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  10. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehiclescited by 92
  11. 2022On Adversarial Robustness of Trajectory Prediction for Autonomous Vehiclescited by 89
  12. 2023The Waymo Open Sim Agents Challengecited by 86
38 more
  1. 2025Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Modelscited by 80
  2. 2023DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Drivingcited by 73
  3. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  4. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  5. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  6. 2022AdvDO: Realistic Adversarial Attacks for Trajectory Predictioncited by 48
  7. 2023Scenario Diffusion: Controllable Driving Scenario Generation With Diffusioncited by 46
  8. 2023RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarioscited by 46
  9. 2021Vehicle trajectory prediction works, but not everywherecited by 39
  10. 2024Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?cited by 38
  11. 2023SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversariescited by 36
  12. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  13. 2023Rethinking Closed-loop Training for Autonomous Drivingcited by 29
  14. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  15. 2024G3R: Gradient Guided Generalizable Reconstructioncited by 29
  16. 2022V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perceptioncited by 28
  17. 2024SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Trafficcited by 27
  18. 2023CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulationcited by 26
  19. 2024CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learningcited by 26
  20. 2023Learning Realistic Traffic Agents in Closed-loopcited by 24
  21. 2024Causal Composition Diffusion Model for Closed-loop Traffic Generationcited by 22
  22. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  23. 2023Reconstructing Objects in-the-wild for Realistic Sensor Simulationcited by 21
  24. 2021CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generationcited by 20
  25. 2023Reinforcement Learning with Human Feedback for Realistic Traffic Simulationcited by 20
  26. 2023LightSim: Neural Lighting Simulation for Urban Scenescited by 20
  27. 2024AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusioncited by 20
  28. 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
  29. 2022CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationshipscited by 18
  30. 2024Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehiclescited by 18
  31. 2023Safety-Critical Scenario Generation Via Reinforcement Learning Based Editingcited by 16
  32. 2025LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Modelscited by 16
  33. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priorscited by 15
  34. 2024Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Modelcited by 14
  35. 2022Deep transfer learning for image classification: a surveycited by 13
  36. 2024FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversarialitycited by 13
  37. 2024CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectoriescited by 11
  38. 2024Learning to Drive via Asymmetric Self-Playcited by 11

50 most cited of 132 All 132 (JSON)