Path Predictions by LSTM Using Object Attributes and Semantic Environment

Abstract

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.