Abstract
To improve foresight and make correct judgment in advance, pedestrian trajectory prediction has a wide range of application values in autonomous driving, robot interaction, and safety monitoring. However, most of the existing methods only focus on the interaction of local pedestrians according to distance, ignoring the influence of far pedestrians; the range of network input (receptive field) is small. In this paper, an extended graph attention network (EGAT) is proposed to increase receptive field, which focuses not only on local pedestrians, but also on those who are far away, to further strengthen pedestrian interaction. In the temporal domain, TSG-LSTM (TS-LSTM and TG-LSTM) and P-LSTM are proposed based on LSTM to enhance information transmission by residual connection. Compared with state-of-the-art methods, the model EGAT achieves excellent performance on both ETH and UCY public datasets and generates more reliable trajectories.
Highlights
Because of complexity and uncertainty of interaction between pedestrian and environment, it is difficult to predict human trajectory
For methods in deep learning, such as Recurrent Neural Network (RNN) [3, 4] and Generative Adversarial Networks (GAN) [5, 6], the human interaction is modeled based on social pooling
Comparison with the State-of-the-Art. e comparison between extended graph attention network (EGAT) and other models is based on five scenarios of ETH and UCY, using evaluation metrics average displacement error (ADE) and final displacement error (FDE) with prediction length of 12. e experimental results show that the performance of the proposed EGAT model is better than most of the methods
Summary
Because of complexity and uncertainty of interaction between pedestrian and environment, it is difficult to predict human trajectory. For methods in deep learning, such as Recurrent Neural Network (RNN) [3, 4] and Generative Adversarial Networks (GAN) [5, 6], the human interaction is modeled based on social pooling. Due to the different influence of adjacent pedestrians on the target pedestrian in trajectory prediction, attention mechanism is more helpful to encode potential pedestrian interaction. On this basis, Graph Attention Network (GAT) [7] comes into being and has been widely applied. Huang et al [9] and Mohamed et al [10] introduced a flexible graph attention mechanism to improve social modeling, but only model the local interaction of close pedestrians
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