# LimSim: A Long-term Interactive Multi-scenario Traffic Simulator

arXiv: https://arxiv.org/abs/2307.06648 · HTML: https://mlsanity.com/paper/2307.06648/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2023.

## Built on by (most cited first, 13 of 13)

- [DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving](https://mlsanity.com/paper/2408.00415/) (2024)
- [HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving](https://mlsanity.com/paper/2412.01718/) (2024)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes](https://mlsanity.com/paper/2409.04003/) (2024)
- [DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation](https://mlsanity.com/paper/2411.11252/) (2024)
- [LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving](https://mlsanity.com/paper/2402.01246/) (2024)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2502.10498/) (2025)
- [RealEngine: Simulating Autonomous Driving in Realistic Context](https://mlsanity.com/paper/2505.16902/) (2025)
- [Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making](https://mlsanity.com/paper/2311.11058/) (2023)
- [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2508.14527/) (2025)
- [SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration](https://mlsanity.com/paper/2512.21618/) (2025)
- [Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization](https://mlsanity.com/paper/2403.02882/) (2024)

## Abstract (arXiv)

With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. LimSim is now open source on GitHub: https://www.github.com/PJLab-ADG/LimSim .