2023 Fiscal Year Research-status Report
Interpretable and Generalizable Pedestrian Trajectory Prediction in Crowds
| Project/Area Number |
23K16897
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| Research Institution | The University of Tokyo |
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Principal Investigator |
史 小丹 東京大学, 空間情報科学研究センター, 客員研究員 (50938909)
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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Keywords | trajectory prediction |
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| Outline of Annual Research Achievements |
A reliable pedestrian trajectory forecasting model in crowds plays a critical role in safety driving of autonomous vehicles. With the success of deep learning, researches are burgeoned to address the problems, but they ignore the interpretability of social interactions and multi-modalities. Besides, generalization of prediction models to unseen scenarios and objects is important to real-world applications, but rarely researched. This research proposes to address the above problems and estimates reasonable pedestrian future trajectories by interpreting and incorporating heterogeneous interacting motion sequences jointly, revealing explainable multi-modal probabilistic distributions of future trajectories and considering the ability to generalize well to unseen scenarios and objects.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Interpretable Social Interactions and generalization are smoothly on going in terms of method development, writing.
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| Strategy for Future Research Activity |
Models for interpreting the social interactions and futural possible locations will be further explored.
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| Causes of Carryover |
I will use the grant for buying computational equipments and use those equipments to develop deep learning models.
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