一种城市道路场景下行人危险度评估方法

A Pedestrian Hazard Assessment Method in Urban Road Scene

针对行人在交通场景对车辆驾驶造成的影响和辅助驾驶需要对行人进行避险的问题,提出一种基于车载单目摄像机的行人危险度评估方法.基于中国城市的特色环境,将行车环境划分为三类:普通道路、人行横道和有辅警道路,对每类场景采用不同的评估方法.采用卷积神经网络,检测视频中道路上的行人、辅警、信号灯和人行道等信息;检测行人关键点并使用多目标跟踪方法,生成骨架姿态时间序列,通过LSTM(长短时记忆神经网络)分析姿态序列获得行人行为和趋势;最后综合视频信息、行人信息和场景信息,构建行人危险评估模型,实现行人危险度评估.实验结果表明,提出的模型可以有效地评估行人危险度,辅助驾驶员安全行车,场景分类使危险模型评估结果更符合行人实际危险度.

In view of the influence of pedestrians on vehicle driving in traffic scenes and the problem that the assisted driving needs to avoid the danger for pedestrians,a pedestrian hazard assessment method based on vehicle-mounted monocular cameras is proposed.Based on the characteristic environment of Chinese cities,this paper divided the driving environment into three scenarios:regular roads,crosswalks,auxiliary roads.Different risk assessment methods are used for each type of scenarios.A convolutional neural network is used to detect and identify pedestrians,auxiliary police,signal lights and sidewalks on the road in the video.Then,it detects the key points of the pedestrian and uses the multi-target tracking method to generate the time series of the pedestrian skeleton.Pedestrian behavior and trend are obtained through LSTM(Long-Short Term Memory Neural Network)analysis of posture sequences.Finally,a pedestrian hazard assessment model is built to realize the pedestrian hazard assessment in multi road scene by synthesizing the video information,pedestrian information and scene information.The experimental results show that the scene classification makes the assessment results of the hazard model more consistent with the actual pedestrian hazard.The proposed model can effectively evaluate the pedestrian hazard and assist the drivers to drive safely.