# A Survey on Simulators for Testing Self-Driving Cars arXiv: https://arxiv.org/abs/2101.05337 ยท HTML: https://mlsanity.com/paper/2101.05337/ ## Key facts - Cited by 27 papers in the map (data through 14 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. ## Placed in - End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/) ## Builds on (most cited first, 8 of 8) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019) - [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020) - [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020) - [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020) - [Key Ingredients of Self-Driving Cars](https://mlsanity.com/paper/1906.02939/) (2019) - [The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles Perspective](https://mlsanity.com/paper/1910.06075/) (2019) - [Distributed Simulation Platform for Autonomous Driving](https://mlsanity.com/paper/1705.10948/) (2017) ## Built on by (most cited first, 27 of 27) - [VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles](https://mlsanity.com/paper/2111.12083/) (2021) - [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023) - [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023) - [VEGS: View Extrapolation of Urban Scenes in 3D Gaussian Splatting using Learned Priors](https://mlsanity.com/paper/2407.02945/) (2024) - [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021) - [Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving](https://mlsanity.com/paper/2202.07133/) (2022) - [CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry](https://mlsanity.com/paper/2211.14054/) (2022) - [OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving](https://mlsanity.com/paper/2402.03830/) (2024) - [Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing](https://mlsanity.com/paper/2305.08060/) (2023) - [A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator](https://mlsanity.com/paper/2509.08221/) (2025) - [From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving](https://mlsanity.com/paper/2305.13960/) (2023) - [A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data](https://mlsanity.com/paper/2407.16680/) (2024) - [Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing](https://mlsanity.com/paper/2203.12026/) (2022) - [An Overview about Emerging Technologies of Autonomous Driving](https://mlsanity.com/paper/2306.13302/) (2023) - [A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness](https://mlsanity.com/paper/2403.15067/) (2024) - [The Pursuit of Diversity: Multi-Objective Testing of Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2510.14727/) (2025) - [Slug Mobile: Test-Bench for RL Testing](https://mlsanity.com/paper/2409.10532/) (2024) - [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025) - [RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos](https://mlsanity.com/paper/2606.16278/) (2026) - [PRIBOOT: A New Data-Driven Expert for Improved Driving Simulations](https://mlsanity.com/paper/2406.08421/) (2024) - [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024) - [Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE](https://mlsanity.com/paper/2502.05037/) (2025) - [MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation](https://mlsanity.com/paper/2505.13959/) (2025) - [TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2507.05932/) (2025) - [Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)](https://mlsanity.com/paper/2511.13397/) (2025) - [AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework](https://mlsanity.com/paper/2606.18532/) (2026) - [Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2607.06484/) (2026) ## 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.