Flow-ERD：面向多样化交通仿真的智能体类型感知流匹配与熵正则化蒸馏方法

摘要

真实且多样化的交通仿真对自动驾驶发展至关重要。然而，现有基准测试主要奖励真实性，近期方法也据此优化，导致多样性尚未得到充分探索。我们提出Flow-ERD，一种同时追求真实性与多样性的多智能体模拟器。其主干——智能体类型感知流匹配（AFM），将流匹配的多模态表达能力与类型特定的运动学执行相结合，在保持运动与各智能体类型一致的同时，保留精细粒度的多样性。第二阶段——熵正则化蒸馏（ERD），通过熵正则化的逆KL散度目标微调闭环轨迹分布，在缓解协变量偏移的同时，明确防止模型坍缩至高密度模式。我们采用无日志多样性度量及标准真实性评分评估Flow-ERD。Flow-ERD在WOSAC测试基准中排名第一，并在可复现基线中主导了真实性与多样性的帕累托前沿。项目页面详见https://seulbinhwang.github.io/flow-erd-project-page/{这里}。

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 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}.