MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Path prediction methods with deep learning architectures take into account the interaction of pedestrians and the features of the physical environment in the surrounding area. These methods, however, process all prediction targets as a unified category and it becomes difficult to predict a path suitable for each category. In real scenes, it is necessary to consider not only pedestrians but also automobiles and bicycles. It is considered possible to predict the path corresponding to the type of target by considering the types of multiple targets. Therefore, aiming to achieve path prediction in accordance with individual categories, we propose a path prediction method that represents the target type as an attribute and simultaneously considers the physical environment information. The proposed method inputs feature vectors in a long short-term memory that rep- resents i ) past object trajectory, ii ) the attribute, and iii ) the semantics of the surrounding area. This makes it possible to predict a path that is proper for each target. Experimental results show that our approach can predict a path with higher precision. Also, changes in accuracy were analyzed by introducing the attribute of the prediction target and the physical environment information.