Pedestrian trajectory prediction via the Social‐Grid LSTM model. Issue 16 (8th November 2018)

• Record Type: Journal Article
• Authors: Cheng, Bang; Xu, Xin; Zeng, Yujun; Ren, Junkai; Jung, Seul
• Is Part Of: Journal of engineering. Volume 2018:Issue 16(2018)
• Page Start: 1468
• Page End: 1474
• Publication Date: 2018-11-08
• Subjects: recurrent neural nets -- learning (artificial intelligence) -- neural nets; intelligent driving systems -- reliable trajectory prediction -- accurate trajectory prediction -- pedestrians -- possible collisions -- intelligent vehicles -- trajectory prediction problem -- sequence learning problem -- recurrent neural network models -- short term memory -- promising method -- RNN architecture -- human–human interactions -- social pooling -- Grid LSTM network model -- baseline methods -- Social LSTM -- performs previous prediction approaches -- pedestrian trajectory prediction -- Social‐Grid LSTM model; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8316
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.