mlsanityData to 7 Oct 2026

A Survey on Simulators for Testing Self-Driving Cars

arXiv 2101.05337 ↗First posted 13 Jan 2021cs.ROcited by 26

On the map: End-to-end autonomous driving → Adversarial scenarios and safety testing

Abstract (arXiv)

A rigorous and comprehensive testing plays a key role in training self-driving cars to handle variety of situations that they are expected to see on public roads. The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap, however, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-the-art simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we also present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 8 of its 48 references that are in the map. First posted on arXiv 13 Jan 2021.

Builds on

8 of 48 references.

References · 8

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  3. 2020LGSVL Simulator: A High Fidelity Simulator for Autonomous Drivingcited by 119
  4. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  5. 2020Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behaviorcited by 23
  6. 2019Key Ingredients of Self-Driving Carscited by 3
  7. 2019The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles Perspectivecited by 3
  8. 2017Distributed Simulation Platform for Autonomous Drivingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 5; 2024: 6; 2025: 7; 2026: 3.

26 papers.

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By area:

  1. 2021VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehiclescited by 81
  2. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  3. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  4. 2024VEGS: View Extrapolation of Urban Scenes in 3D Gaussian Splatting using Learned Priorscited by 25
  5. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  6. 2022Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Drivingcited by 20
  7. 2022CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industrycited by 10
  8. 2024OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Drivingcited by 10
  9. 2023Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testingcited by 8
  10. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulatorcited by 7
  11. 2023From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Drivingcited by 6
  12. 2022Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testingcited by 4
14 more
  1. 2023An Overview about Emerging Technologies of Autonomous Drivingcited by 2
  2. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Datacited by 2
  3. 2024Slug Mobile: Test-Bench for RL Testingcited by 1
  4. 2025Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safetycited by 1
  5. 2025The Pursuit of Diversity: Multi-Objective Testing of Deep Reinforcement Learning Agentscited by 1
  6. 2026RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videoscited by 1
  7. 2024PRIBOOT: A New Data-Driven Expert for Improved Driving Simulationscited by 0
  8. 2024Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Reportcited by 0
  9. 2025Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATEcited by 0
  10. 2025MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validationcited by 0
  11. 2025TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systemscited by 0
  12. 2025Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)cited by 0
  13. 2026AI Sandboxes: A Threat Model, Taxonomy, and Measurement Frameworkcited by 0
  14. 2026Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehiclescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions
  2. 2021Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test
  3. 2025A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems
  4. 2025AI-Augmented Metamorphic Testing for Comprehensive Validation of Autonomous Vehicles ↗
  5. 2024From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events
  6. 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving
  7. 2024Generating Critical Scenarios for Testing Automated Driving Systems
  8. 2023Data-driven Traffic Simulation: A Comprehensive Review