Human trajectory prediction for automatic guided vehicle with recurrent neural network
文献类型：会议论文
| 作者 | Chao Song; Zhixian Chen; Xiaozhi Qi; Baoliang Zhao; Ying Hu; Shoubin Liu; Jianwei Zhang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Asian |
| 英文摘要 | The accurate prediction of the pedestrian trajectory is necessary to endow automatic guided vehicle with the capabilities to adjust velocity and path dynamically for the navigation in real pedestrian scenes. For this purpose, this study presents a social conscious prediction model considering two main factors that affect the pedestrians’ walking in the crowd – relative distance and moving direction. To form an effective model, the authors’ conscious pooling layer is added to the Long Shot Term Memory network (LTSM) model to build the relationship between pedestrians, learning the current position m and movement trend. The experiments are conducted to compare the proposed model with the previous state-of-the-art model on several public datasets. The experimental results show that the proposed model predicts pedestrian trajectories more accurately. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13764]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Chao Song,Zhixian Chen,Xiaozhi Qi,et al. Human trajectory prediction for automatic guided vehicle with recurrent neural network[C]. 见:. Asian. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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