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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.20353 |
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| _version_ | 1866911122260492288 |
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| author | Harder, Aron Behl, Madhur |
| author_facet | Harder, Aron Behl, Madhur |
| contents | Scenario Description Languages (SDLs) provide structured, interpretable embeddings that represent traffic scenarios encountered by autonomous vehicles (AVs), supporting key tasks such as scenario similarity searches and edge case detection for safety analysis. This paper introduces the Trajectory-to-Action Pipeline (TAP), a scalable and automated method for extracting SDL labels from large trajectory datasets. TAP applies a rules-based cross-entropy optimization approach to learn parameters directly from data, enhancing generalization across diverse driving contexts. Using the Waymo Open Motion Dataset (WOMD), TAP achieves 30% greater precision than Average Displacement Error (ADE) and 24% over Dynamic Time Warping (DTW) in identifying behaviorally similar trajectories. Additionally, TAP enables automated detection of unique driving behaviors, streamlining safety evaluation processes for AV testing. This work provides a foundation for scalable scenario-based AV behavior analysis, with potential extensions for integrating multi-agent contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trajectory-to-Action Pipeline (TAP): Automated Scenario Description Extraction for Autonomous Vehicle Behavior Comparison Harder, Aron Behl, Madhur Robotics Scenario Description Languages (SDLs) provide structured, interpretable embeddings that represent traffic scenarios encountered by autonomous vehicles (AVs), supporting key tasks such as scenario similarity searches and edge case detection for safety analysis. This paper introduces the Trajectory-to-Action Pipeline (TAP), a scalable and automated method for extracting SDL labels from large trajectory datasets. TAP applies a rules-based cross-entropy optimization approach to learn parameters directly from data, enhancing generalization across diverse driving contexts. Using the Waymo Open Motion Dataset (WOMD), TAP achieves 30% greater precision than Average Displacement Error (ADE) and 24% over Dynamic Time Warping (DTW) in identifying behaviorally similar trajectories. Additionally, TAP enables automated detection of unique driving behaviors, streamlining safety evaluation processes for AV testing. This work provides a foundation for scalable scenario-based AV behavior analysis, with potential extensions for integrating multi-agent contexts. |
| title | Trajectory-to-Action Pipeline (TAP): Automated Scenario Description Extraction for Autonomous Vehicle Behavior Comparison |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.20353 |