# InterSim: Interactive Traffic Simulation via Explicit Relation Modeling

arXiv: https://arxiv.org/abs/2210.14413 · HTML: https://mlsanity.com/paper/2210.14413/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2022.

## Built on by (most cited first, 27 of 27)

- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS](https://mlsanity.com/paper/2408.01584/) (2024)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Data-driven Traffic Simulation: A Comprehensive Review](https://mlsanity.com/paper/2310.15975/) (2023)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [Language-Driven Interactive Traffic Trajectory Generation](https://mlsanity.com/paper/2405.15388/) (2024)
- [Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills](https://mlsanity.com/paper/2309.13614/) (2023)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [Uncertainty-Aware Decision Transformer for Stochastic Driving Environments](https://mlsanity.com/paper/2309.16397/) (2023)
- [TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters](https://mlsanity.com/paper/2405.04491/) (2024)
- [Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making](https://mlsanity.com/paper/2311.11058/) (2023)
- [RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation](https://mlsanity.com/paper/2505.03344/) (2025)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Getting SMARTER for Motion Planning in Autonomous Driving Systems](https://mlsanity.com/paper/2502.15824/) (2025)
- [P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving](https://mlsanity.com/paper/2211.01634/) (2022)
- [LitSim: A Conflict-aware Policy for Long-term Interactive Traffic Simulation](https://mlsanity.com/paper/2403.04299/) (2024)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models](https://mlsanity.com/paper/2502.10012/) (2025)
- [Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization](https://mlsanity.com/paper/2403.02882/) (2024)
- [MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulation](https://mlsanity.com/paper/2404.18464/) (2024)
- [Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline](https://mlsanity.com/paper/2504.14709/) (2025)
- [A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios](https://mlsanity.com/paper/2510.02627/) (2025)
- [Is Your Trajectory Displacement Safe in Long-tail?](https://mlsanity.com/paper/2606.16313/) (2026)

## Abstract (arXiv)

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existing approaches learn an agent model from large-scale driving data to simulate realistic traffic scenarios, yet it remains an open question to produce consistent and diverse multi-agent interactive behaviors in crowded scenes. In this work, we present InterSim, an interactive traffic simulator for testing autonomous driving planners. Given a test plan trajectory from the ego agent, InterSim reasons about the interaction relations between the agents in the scene and generates realistic trajectories for each environment agent that are consistent with the relations. We train and validate our model on a large-scale interactive driving dataset. Experiment results show that InterSim achieves better simulation realism and reactivity in two simulation tasks compared to a state-of-the-art learning-based traffic simulator.