SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913221789614080 |
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| author | Stoler, Benjamin Navarro, Ingrid Jana, Meghdeep Hwang, Soonmin Francis, Jonathan Oh, Jean |
| author_facet | Stoler, Benjamin Navarro, Ingrid Jana, Meghdeep Hwang, Soonmin Francis, Jonathan Oh, Jean |
| contents | As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Motion, provide realistic recorded scenarios for model development, they often lack truly safety-critical situations. Rather than utilizing unrealistic simulation or dangerous real-world testing, we instead propose a framework to characterize such datasets and find hidden safety-relevant scenarios within. Our approach expands the spectrum of safety-relevance, allowing us to study trajectory prediction models under a safety-informed, distribution shift setting. We contribute a generalized scenario characterization method, a novel scoring scheme to find subtly-avoided risky scenarios, and an evaluation of trajectory prediction models in this setting. We further contribute a remediation strategy, achieving a 10% average reduction in prediction collision rates. To facilitate future research, we release our code to the public: github.com/cmubig/SafeShift |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08889 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving Stoler, Benjamin Navarro, Ingrid Jana, Meghdeep Hwang, Soonmin Francis, Jonathan Oh, Jean Robotics As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Motion, provide realistic recorded scenarios for model development, they often lack truly safety-critical situations. Rather than utilizing unrealistic simulation or dangerous real-world testing, we instead propose a framework to characterize such datasets and find hidden safety-relevant scenarios within. Our approach expands the spectrum of safety-relevance, allowing us to study trajectory prediction models under a safety-informed, distribution shift setting. We contribute a generalized scenario characterization method, a novel scoring scheme to find subtly-avoided risky scenarios, and an evaluation of trajectory prediction models in this setting. We further contribute a remediation strategy, achieving a 10% average reduction in prediction collision rates. To facilitate future research, we release our code to the public: github.com/cmubig/SafeShift |
| title | SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.08889 |