基于图表示的智能车行人意图识别方法

智能驾驶场景下的人车冲突问题与行人过街行为密切相关，为使高级驾驶辅助系统（advanced driving assistance system, ADAS）具备识别行人过街意图的功能，并对人车碰撞事件预警，提出一种基于图表示学习（graph representation learning, GRL）方法的行人过街意图识别框架。它采用开源工具对行人骨架信息进行识别，采用图方法，以行人在一段运动过程内每一帧的骨架关键点为节点，以骨架自然连接关系、相关关系和时域关系为边建立图模型，实现对行人动作序列的表征。以图结构数据为输入，基于支持向量机（support vector machine, SVM）训练行人过街意图识别模型。在自动驾驶数据集PIE上对所提出方法进行评估，结果显示，行人过街意图分类准确率可达90.29%，所提出方法能够有效识别行人过街意图，对提高智能车决策安全性具有重要意义。 

Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle

The problem of pedestrian-vehicle conflict in intelligent driving scenes is closely related to pedestrian crossing behavior. In order to enable advanced driving assistance system (ADAS) to have the function of identifying pedestrian crossing intentions and raising advanced warning of pedestrian-vehicle collision events, a pedestrian crossing intention recognition framework based on graph representation learning (GRL) method is proposed. It uses open source tools to generate pedestrian skeleton information. Then it establishes a graph model to represent the characteristics of pedestrian action sequence by taking the skeleton key points of each frame of pedestrian within a sequence as nodes, as well as taking the natural connections, the topological correlations and time-domain relationships between skeleton joints as edges. Taking the graph structure data as the input, the pedestrian crossing intention recognition model is trained based on support vector machine (SVM). The results show that the classification accuracy of pedestrian crossing intention can reach 90.29%. The proposed method can effectively identify the pedestrian crossing intention, which is of great significance to improve the safety of intelligent vehicle decision-making.