精密工学会誌
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
画像技術の実利用特集論文
移動対象の属性と環境情報を導入したLSTMによる経路予測
箕浦 大晃平川 翼山下 隆義藤吉 弘亘
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2020 年 86 巻 12 号 p. 961-968

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Path prediction methods with deep learning architectures consider the interactions of pedestrians with the feature of the surrounding physical environment. However, these methods process all pedestrian targets as a unified category, making it difficult to predict a suitable path for each category. In real scenes, both pedestrians and vehicles must be considered. Predicting the path that corresponds to a target type is possible by considering the types of multiple targets. Therefore, to achieve path prediction compatible with individual categories, we propose a path prediction method that simultaneously represents the target type as an attribute and considers physical environment information. Our method inputs feature vectors that represent i) past object trajectory, ii) the attribute, and iii) the semantics of the surrounding area into a long short-term memory, making it possible to predict a proper path for each target. Experiments prove that our approach can predict a path with higher precision. Also, we analyze its effectiveness by introducing the attribute of the prediction target and the physical environment's information.

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