Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation
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| Authors | Seulbin Hwang et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2607.06957 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Realistic and diverse traffic simulation is essential to autonomous driving development. Yet prevailing benchmarks predominantly reward realism, and recent methods have optimized accordingly, leaving diversity underexplored. We introduce Flow-ERD, a multi-agent simulator that pursues realism and diversity jointly. Its backbone, Agent-Type Aware Flow Matching (AFM), couples flow matching's multi-modal expressiveness with type-specific kinematic execution. It preserves fine-grained diversity while keeping motions consistent with each agent type. A second stage, Entropy-Regularized Distillation (ERD), fine-tunes the closed-loop rollout distribution with an entropy-regularized reverse-KL objective. This mitigates covariate shift while explicitly preventing collapse onto high-density modes. We evaluate Flow-ERD with a log-free diversity metric alongside standard realism scores. Flow-ERD ranks first on the WOSAC test benchmark and dominates the realism--diversity Pareto front among reproducible baselines. Our project page is available https://seulbinhwang.github.io/flow-erd-project-page/{here}.
Engineering Breakdown
The Problem
Realistic and diverse traffic simulation is essential to autonomous driving development.
The Approach
We introduce Flow-ERD, a multi-agent simulator that pursues realism and diversity jointly.
Key Results
Our project page is available https://seulbinhwang.github.io/flow-erd-project-page/{here}.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Agenttype
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