LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917078849552384 |
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| author | Stoler, Benjamin Francis, Jonathan Oh, Jean |
| author_facet | Stoler, Benjamin Francis, Jonathan Oh, Jean |
| contents | Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To assess robustness under such conditions, we propose new long-tail evaluation settings that repartition datasets to create challenging out-of-distribution (OOD) test sets. We first introduce a safety-informed scenario factorization framework, which disentangles scenarios into discrete ego and social contexts. Building on analogies to compositional zero-shot image-labeling in Computer Vision, we then hold out novel context combinations to construct challenging closed-world and open-world settings. This process induces OOD performance gaps in future motion prediction of 5.0% and 14.7% in closed-world and open-world settings, respectively, relative to in-distribution performance for a state-of-the-art baseline. To improve generalization, we extend task-modular gating networks to operate within trajectory prediction models, and develop an auxiliary, difficulty-prediction head to refine internal representations. Our strategies jointly reduce the OOD performance gaps to 2.8% and 11.5% in the two settings, respectively, while still improving in-distribution performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction Stoler, Benjamin Francis, Jonathan Oh, Jean Robotics Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To assess robustness under such conditions, we propose new long-tail evaluation settings that repartition datasets to create challenging out-of-distribution (OOD) test sets. We first introduce a safety-informed scenario factorization framework, which disentangles scenarios into discrete ego and social contexts. Building on analogies to compositional zero-shot image-labeling in Computer Vision, we then hold out novel context combinations to construct challenging closed-world and open-world settings. This process induces OOD performance gaps in future motion prediction of 5.0% and 14.7% in closed-world and open-world settings, respectively, relative to in-distribution performance for a state-of-the-art baseline. To improve generalization, we extend task-modular gating networks to operate within trajectory prediction models, and develop an auxiliary, difficulty-prediction head to refine internal representations. Our strategies jointly reduce the OOD performance gaps to 2.8% and 11.5% in the two settings, respectively, while still improving in-distribution performance. |
| title | LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.10411 |