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Title	Multi-Relational Pedestrian Trajectory Prediction In Complex Scenes
ID_Doc	38363
Authors	Peng W.; Cui Z.; Duan Y.; Tao X.
Year	2022
Published	IEEE Vehicular Technology Conference, 2022-September
DOI	http://dx.doi.org/10.1109/VTC2022-Fall57202.2022.10012858
Abstract	Pedestrian trajectory prediction has an important impact on the construction of smart cities and the popularization of autonomous vehicles. Pedestrian trajectory prediction in complex scenes is challenging because the trajectories are largely disturbed by the surrounding social environment. To better model the relationship between pedestrians and the social environment, we propose a novel Multi-Relation Network based on Long Short-Term Memory(LSTM). We first use Convolution Neural Network(CNN) and LSTM for feature extraction of scenes and pedestrians respectively. We realize the interaction between pedestrians and social environment by introducing attention mechanism. The experiment results on two public datasets, i.e. ETH and UCY, demonstrate that the prediction accuracy can be effectively improved by considering the social interactions. © 2022 IEEE.
Author Keywords	Attention Mechanism; Interaction; Long Short-Term Memory(LSTM); Pedestrian Trajectory Prediction

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