Open AccessPedestrian trajectory prediction via the Social‐Grid LSTM model
Author(s) -
Cheng Bang,
Xu Xin,
Zeng Yujun,
Ren Junkai,
Jung Seul
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8316
Subject(s) - pooling , computer science , trajectory , pedestrian , recurrent neural network , artificial intelligence , grid , machine learning , sequence (biology) , key (lock) , artificial neural network , engineering , physics , geometry , mathematics , computer security , astronomy , biology , transport engineering , genetics
In the design of intelligent driving systems, reliable and accurate trajectory prediction of pedestrians is necessary. With the prediction of pedestrians’ trajectory, the possible collisions can be avoided or warned as early as possible by changing the behaviour of intelligent vehicles. The trajectory prediction problem can be considered as a sequence learning problem, in which one of the recurrent neural network (RNN) models called long short term memory (LSTM) has been regarded as a promising method. The authors present a new method for predicting the pedestrian's trajectory, which is called Social‐Grid LSTM based on RNN architecture. The proposed method combines the human–human interaction model called social pooling and the Grid LSTM network model. The performance of the proposed method is demonstrated on two available public datasets, and compared with two baseline methods (LSTM and Social LSTM). The experimental results indicate that the authors’ proposed method outperforms previous prediction approaches.