基于行人视野注意力场的人车微观交互模型

Pedestrian-Vehicle Micro-Interaction Model Based on Attention Field of Pedestrian Vision

从行人视觉认知角度出发,提出一种基于行人视野注意力场的人车微观交互模型。构建视野注意力场驱动行人视野域,利用人工势场驱动行人运动,利用目标捕捉算法来控制行人视野域对目标的捕捉。为了验证模型的有效性,使用无人机采集鸟瞰视角下的人车交互数据并进行处理分析,将行人过街风格分为保守、谨慎和冒险3种类型,在Pygame平台下搭建仿真场景和交互模型,把不同行人过街风格的交互数据作为模型输入,以模型输出的行人时空轨迹与采集的真实时空轨迹之间的相似度进行实验对比。结果表明,建立的基于行人视野注意力场的人车微观交互模型比常规人工势场模型准确性提高了25.48%,能够有效地复现实际交通场景中的人车交互过程。

From the perspective of pedestrian visual cognition,a pedestrian-vehicle micro-interaction model based on the atten-tion field of pedestrian vision is proposed. The attention field of pedestrian vision is constructed to drive the pedes-trian field of vision,and the artificial potential field is used to drive the pedestrian movement,.The target capture algorithm is used to control the target capture in the pedestrian visual field. In order to verify the effectiveness of the model,drones are used to collect the pedestrian-vehicle interaction data from the bird’s-eye perspective and analyze them. Pedestrian crossing styles are divided into three types:conservative,cautious and adventurous. Simulation scenarios and interactive models are built on the Pygame platform,then,different types of interactive data are used as the model input,the similarity between the pedestrian spatiotemporal trajectory output by the model and the collected real spatiotemporal trajectory is experimentally compared. The results show that the pedestrian-vehicle mi-cro-interaction model based on pedestrian visual attention field is 25.48% more accurate than the conventional artifi-cial potential field model,and it can effectively reproduce the pedestrian-vehicle interaction process in the actual traf-fic scene.