基于双流自适应图卷积神经网络的行人过街意图识别

Pedestrian-crossing intention-recognition based on dual-stream adaptive graph-convolutional neural-network

对城市道路上的自动驾驶车辆,提出了一种判别行人过街意图的识别方法。该方法利用双流、时空自适应图卷积神经网络(2s-AGCN),联系了行人骨架的动力学与行人过街意图;以时空图卷积神经网络(ST-GCN)的动作识别为基础,加入自适应图卷积神经网络结构(AGCN);在骨骼的长度和方向上,设计了双流网络,将2个网络输出的Softmax分数融合,来预测行人过街意图。根据自动驾驶联合注意力公开数据集(JAAD),进行了仿真实验。结果表明:本文的2s-AGCN行人过街意图识别方法的准确率达到了89.36%,比ST-GCN神经网络的结果高3.36%。因此,该方法识别准确率较高。

A recognition method was proposed to judge pedestrians’intention to cross street for autonomous vehicles on urban roads.The method utilized a two-stream,spatiotemporally adaptive graph-convolutional neural-network(named 2s-AGCN)with linking the dynamics of pedestrian skeletons and pedestrian crossing intention;Added the adaptive graph convolutional neural-network(AGCN)structure based on the action recognition of the spatiotemporal graph convolutional neural network(ST-GCN);A dual-stream neural-network was designed in terms of the length and direction of the bones for fusing the Softmax scores output by the two networks to predict pedestrian crossing intention.Simulation experiments were carried out based on the Joint Attention in Autonomous Driving public Dataset(JAAD).The results shown that the accuracy of this 2s-AGCN method reaches 89.36%,which is 3.36% higher than the accuracy of the ST-GCN.Therefore,the recognition accuracy of this method is high.