对比传统模型与数据驱动仿真，发现SUMO在长时稳定性和参数少方面更优。
Systematic Benchmarking of SUMO Against Data-Driven Traffic Simulators
- 用自动化工具将真实数据转为SUMO仿真环境
- 60秒长时仿真中碰撞率低、不偏离道路
- 适合需要少参数且稳定性的自动驾驶测试
本文针对基于模型的微观交通仿真器SUMO，使用大规模真实世界数据集（Waymo Open Motion Dataset, WOMD）和Waymo Open Sim Agents Challenge (WOSAC)进行系统性对比评估。通过开发Waymo2SUMO自动化转换管道，将真实场景导入SUMO，评估其在短时（8秒）与长时（60秒）闭环仿真下的表现。在WOSAC基准上，SUMO实现0.653的真实感元指标，且仅需不到100个可调参数。长时间滚动测试显示，SUMO保持低碰撞率与离道率，长期稳定性优于典型数据驱动仿真器。结果表明，模型驱动与数据驱动方法在自动驾驶仿真中具有互补优势。
原文摘要 · Abstract (English)
This paper presents a systematic benchmarking of the model-based microscopic traffic simulator SUMO against state-of-the-art data-driven traffic simulators using large-scale real-world datasets. Using the Waymo Open Motion Dataset (WOMD) and the Waymo Open Sim Agents Challenge (WOSAC), we evaluate SUMO under both short-horizon (8s) and long-horizon (60s) closed-loop simulation settings. To enable scalable evaluation, we develop Waymo2SUMO, an automated pipeline that converts WOMD scenarios into SUMO simulations. On the WOSAC benchmark, SUMO achieves a realism meta metric of 0.653 while requiring fewer than 100 tunable parameters. Extended rollouts show that SUMO maintains low collision and offroad rates and exhibits stronger long-horizon stability than representative data-driven simulators. These results highlight complementary strengths of model-based and data-driven approaches for autonomous driving simulation and benchmarking.
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