Systematic Benchmarking of SUMO Against Data-Driven Traffic Simulators
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911329472741376 |
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| author | Liang, Erdao |
| author_facet | Liang, Erdao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18537 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Systematic Benchmarking of SUMO Against Data-Driven Traffic Simulators Liang, Erdao Robotics 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. |
| title | Systematic Benchmarking of SUMO Against Data-Driven Traffic Simulators |
| topic | Robotics |
| url | https://arxiv.org/abs/2512.18537 |