让自动驾驶仿真同时更真实且更多样，突破传统只重真实性的局限。
Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation
- 用分类型运动建模+流匹配，保留不同车辆的细节动作差异。
- 引入熵正则化蒸馏，避免仿真结果集中在少数模式上。
- 在真实性和多样性权衡中表现最优，适合自动驾驶测试场景。
真实的多样化交通仿真对自动驾驶开发至关重要。现有基准主要奖励真实性，导致近期方法过度优化此目标，忽视了多样性。我们提出Flow-ERD，一种兼顾真实与多样的多智能体仿真器。其核心为分类型流匹配（AFM），将流匹配的多模态表达能力与特定车型的动力学执行相结合，既保留精细多样性，又确保动作符合各类别特征。第二阶段采用熵正则化蒸馏（ERD），通过熵正则化的反KL目标微调闭环回放分布，缓解协变量偏移，显式防止退化到高密度模式。我们在无日志多样性度量下评估，并结合标准真实性指标。Flow-ERD在WOSAC测试基准上排名第一，在可复现基线中全面占据真实-多样性的帕累托前沿。
原文摘要 · Abstract (English)
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 \textbf{Flow-ERD}, a multi-agent simulator that pursues realism and diversity jointly. Its backbone, \textbf{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, \textbf{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 \href{https://seulbinhwang.github.io/flow-erd-project-page/}{here}.
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