# Versatile Behavior Diffusion for Generalized Traffic Agent Simulation

arXiv: https://arxiv.org/abs/2404.02524 · HTML: https://mlsanity.com/paper/2404.02524/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

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

- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://mlsanity.com/paper/2503.22496/) (2025)
- [Promptable Closed-loop Traffic Simulation](https://mlsanity.com/paper/2409.05863/) (2024)
- [Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning](https://mlsanity.com/paper/2509.23993/) (2025)
- [UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation](https://mlsanity.com/paper/2501.17015/) (2025)
- [DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy](https://mlsanity.com/paper/2505.19516/) (2025)
- [ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable](https://mlsanity.com/paper/2409.07830/) (2024)
- [Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset](https://mlsanity.com/paper/2504.17371/) (2025)
- [When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks](https://mlsanity.com/paper/2510.14677/) (2025)
- [Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model](https://mlsanity.com/paper/2507.22615/) (2025)
- [VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness](https://mlsanity.com/paper/2601.12672/) (2026)
- [Surprise Potential as a Measure of Interactivity in Driving Scenarios](https://mlsanity.com/paper/2502.05677/) (2025)
- [Human-like autonomy emerges from self-play and a pinch of human data](https://mlsanity.com/paper/2606.19370/) (2026)
- [DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing](https://mlsanity.com/paper/2411.09451/) (2024)
- [Controllable Motion Generation via Diffusion Modal Coupling](https://mlsanity.com/paper/2503.02353/) (2025)
- [Optimization-Guided Diffusion for Interactive Scene Generation](https://mlsanity.com/paper/2512.07661/) (2025)
- [SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving](https://mlsanity.com/paper/2604.11734/) (2026)
- [Heterogeneous Self-Play for Realistic Highway Traffic Simulation](https://mlsanity.com/paper/2604.16406/) (2026)
- [RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2605.19033/) (2026)
- [OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model](https://mlsanity.com/paper/2606.30421/) (2026)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://mlsanity.com/paper/2512.02844/) (2025)
- [AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2603.02542/) (2026)
- [Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic](https://mlsanity.com/paper/2605.19038/) (2026)
- [Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation](https://mlsanity.com/paper/2606.27123/) (2026)
- [Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling](https://mlsanity.com/paper/2606.31844/) (2026)
- [ECoSim: Data Efficient Fine-Tuning for Controllable Traffic Simulation](https://mlsanity.com/paper/2607.00545/) (2026)
- [Controllable Sim Agents with Behavior Latents](https://mlsanity.com/paper/2607.02496/) (2026)
- [Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation](https://mlsanity.com/paper/2607.06957/) (2026)

## Abstract (arXiv)

Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework based on diffusion generative models that synthesizes scene-consistent, realistic, and controllable multi-agent interactions. VBD achieves strong performance in closed-loop traffic simulation, generating scene-consistent agent behaviors that reflect complex agent interactions. A key capability of VBD is inference-time scenario editing through multi-step refinement, guided by behavior priors and model-based optimization objectives, enabling flexible and controllable behavior generation. Despite being trained on real-world traffic datasets with only normal conditions, we introduce conflict-prior and game-theoretic guidance approaches. These approaches enable the generation of interactive, customizable, or long-tail safety-critical scenarios, which are essential for comprehensive testing and validation of autonomous driving systems. Extensive experiments validate the effectiveness and versatility of VBD and highlight its promise as a foundational tool for advancing traffic simulation and autonomous vehicle development. Project website: https://sites.google.com/view/versatile-behavior-diffusion