# Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving

arXiv: https://arxiv.org/abs/2311.11056 · HTML: https://mlsanity.com/paper/2311.11056/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 160 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 30 of 65)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [UniSim: A Neural Closed-Loop Sensor Simulator](https://mlsanity.com/paper/2308.01898/) (2023)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Meta-Sim: Learning to Generate Synthetic Datasets](https://mlsanity.com/paper/1904.11621/) (2019)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks](https://mlsanity.com/paper/1909.00321/) (2019)

## Built on by (most cited first, 26 of 26)

- [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://mlsanity.com/paper/2503.22496/) (2025)
- [LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting](https://mlsanity.com/paper/2410.05111/) (2024)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis](https://mlsanity.com/paper/2406.18360/) (2024)
- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [Getting SMARTER for Motion Planning in Autonomous Driving Systems](https://mlsanity.com/paper/2502.15824/) (2025)
- [Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation](https://mlsanity.com/paper/2503.11731/) (2025)
- [SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation](https://mlsanity.com/paper/2407.12788/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [RGE-GS: Reward-Guided Expansive Driving Scene Reconstruction via Diffusion Priors](https://mlsanity.com/paper/2506.22800/) (2025)
- [UrbanTwin: Synthetic Roadside LiDAR Datasets](https://mlsanity.com/paper/2509.06781/) (2025)
- [Physics-Aware 3D Gaussian Editing for Driving Scene Generation](https://mlsanity.com/paper/2605.25373/) (2026)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025)
- [Comprehensive Assessment of LiDAR Evaluation Metrics: A Comparative Study Using Simulated and Real Data](https://mlsanity.com/paper/2511.02994/) (2025)
- [RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation](https://mlsanity.com/paper/2608.11580/) (2026)
- [PRIBOOT: A New Data-Driven Expert for Improved Driving Simulations](https://mlsanity.com/paper/2406.08421/) (2024)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation](https://mlsanity.com/paper/2504.18010/) (2025)
- [UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluation](https://mlsanity.com/paper/2509.02903/) (2025)
- [High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception](https://mlsanity.com/paper/2509.02904/) (2025)
- [Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic](https://mlsanity.com/paper/2604.12857/) (2026)
- [ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation](https://mlsanity.com/paper/2604.17147/) (2026)
- [Agent-driven Long-tail Simulation for Autonomous Driving](https://mlsanity.com/paper/2607.04331/) (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)
- [SV2V-RSim: A Comprehensive Benchmark for Self-Selective V2V Cooperative Perception with Near-Realistic Data](https://mlsanity.com/paper/2609.32863/) (2026)

## Abstract (arXiv)

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To bridge the gap in research, this paper analyzes the evolution of simulators and explains how the functionalities and utilities have developed. Then, the existing simulators are categorized based on their task applicability, providing researchers with a taxonomy to swiftly assess a simulator's suitability for specific tasks. Recommendations for select simulators are presented, considering factors such as accessibility, maintenance status, and quality. Recognizing potential hazards in simulators that could impact the confidence of simulation experiments, the paper dedicates substantial effort to identifying and justifying critical issues in actively maintained open-source simulators. Moreover, the paper reviews potential solutions to address these issues, serving as a guide for enhancing the credibility of simulators.