| Project/Area Number |
23K16897
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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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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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2025: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2024: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2023: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | trajectory prediction / pedestrian / trajectory / prediction |
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| Outline of Research at the Start |
This research proposes to estimate reasonable, reliable and robust 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. The research attempts to promote road safety of automated driving and yield social benefits.
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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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