基于社会注意力机制的行人轨迹预测方法研究

Research on pedestrian trajectory prediction method based on social attention mechanism

为提高行人交互中轨迹预测速度、精度与模型可解释性,提出了一种基于社会注意力机制的GAN模型。首先,定义了一种新型社会关系,对行人间的影响进行社会关系建模,设计了基于注意力机制的网络模型,提高了网络预测速度和可解释性。然后,探索不同池化汇集机制对预测结果的影响,确定性能优异的池化模型。最后,搭建了轨迹预测网络,并在UCY和ETH数据集中进行训练。实验结果表明,所提模型预测精度优于现有方法,且实时性较现有方法提升18.3%。

In order to improve the speed,accuracy and model interpretability of trajectory prediction in pedestrian interaction,a GAN model based on social attention mechanism was proposed.Firstly,a new type of social relationship on pedestrians was defined to model social relationships and a network model based on the attention mechanism was designed to improve the speed and interpretability of network prediction.Secondly,the influence of different pooling mechanisms on the prediction results was explored to determine the pooling model with excellent performance.Finally,a trajectory prediction network was built on this basis and trained on the UCY and ETH data sets.The experimental results show that the model not only has better prediction accuracy than the existing methods,but also improves the real-time performance by 18.3%compared with the existing methods.