# TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios

arXiv: https://arxiv.org/abs/2210.06609 · HTML: https://mlsanity.com/paper/2210.06609/

## Key facts

- Cited by 106 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2022.

## Built on by (most cited first, 30 of 106)

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [LMDrive: Closed-Loop End-to-End Driving with Large Language Models](https://mlsanity.com/paper/2312.07488/) (2023)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation](https://mlsanity.com/paper/2403.06845/) (2024)
- [Street-View Image Generation from a Bird's-Eye View Layout](https://mlsanity.com/paper/2301.04634/) (2023)
- [OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2412.15208/) (2024)
- [Language Conditioned Traffic Generation](https://mlsanity.com/paper/2307.07947/) (2023)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [LidarDM: Generative LiDAR Simulation in a Generated World](https://mlsanity.com/paper/2404.02903/) (2024)
- [Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion](https://mlsanity.com/paper/2311.02738/) (2023)
- [RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios](https://mlsanity.com/paper/2312.13303/) (2023)
- [Solving Motion Planning Tasks with a Scalable Generative Model](https://mlsanity.com/paper/2407.02797/) (2024)
- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction](https://mlsanity.com/paper/2403.11492/) (2024)
- [DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation](https://mlsanity.com/paper/2411.11252/) (2024)
- [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)
- [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://mlsanity.com/paper/2503.22496/) (2025)
- [DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch](https://mlsanity.com/paper/2309.14685/) (2023)
- [Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving](https://mlsanity.com/paper/2409.18343/) (2024)
- [Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023](https://mlsanity.com/paper/2306.11868/) (2023)
- [Learning Realistic Traffic Agents in Closed-loop](https://mlsanity.com/paper/2311.01394/) (2023)
- [SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models](https://mlsanity.com/paper/2311.15736/) (2023)
- [Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency](https://mlsanity.com/paper/2506.07497/) (2025)
- [Reinforcement Learning with Human Feedback for Realistic Traffic Simulation](https://mlsanity.com/paper/2309.00709/) (2023)
- [Promptable Closed-loop Traffic Simulation](https://mlsanity.com/paper/2409.05863/) (2024)
- [AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion](https://mlsanity.com/paper/2410.08453/) (2024)
- [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://mlsanity.com/paper/2506.21976/) (2025)
- [Language-Driven Interactive Traffic Trajectory Generation](https://mlsanity.com/paper/2405.15388/) (2024)

## Abstract (arXiv)

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called TrafficGen for traffic scenario generation. It learns from the fragmented human driving data collected in the real world and then can generate realistic traffic scenarios. TrafficGen is an autoregressive generative model with an encoder-decoder architecture. In each autoregressive iteration, it first encodes the current traffic context with the attention mechanism and then decodes a vehicle's initial state followed by generating its long trajectory. We evaluate the trained model in terms of vehicle placement and trajectories and show substantial improvements over baselines. TrafficGen can be also used to augment existing traffic scenarios, by adding new vehicles and extending the fragmented trajectories. We further demonstrate that importing the generated scenarios into a simulator as interactive training environments improves the performance and the safety of driving policy learned from reinforcement learning. More project resource is available at https://metadriverse.github.io/trafficgen