| 研究課題/領域番号 |
23K16897
|
|---|
| 研究種目 |
若手研究
|
| 配分区分 | 基金 |
|---|
| 審査区分 |
小区分61010:知覚情報処理関連
|
|---|
| 研究機関 | 東京大学 |
|---|
研究代表者 |
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)
|
|---|
| 研究期間 (年度) |
2023-04-01 – 2026-03-31
|
| 研究課題ステータス |
交付 (2023年度)
|
| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2025年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2024年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2023年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
|
|---|
| キーワード | trajectory prediction / pedestrian / trajectory / prediction |
|---|
| 研究開始時の研究の概要 |
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.
|
| 研究実績の概要 |
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.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Interpretable Social Interactions and generalization are smoothly on going in terms of method development, writing.
|
| 今後の研究の推進方策 |
Models for interpreting the social interactions and futural possible locations will be further explored.
|
