基于无人机视频的非机动车道交通冲突分析

Traffic Conflict Analysis of Non-Motor Vehicle Lanes Based on UAV Video

基于轨迹数据的交通冲突分析是道路交通流状态及安全风险评价的重要途径,针对当前电动自行车对混行交通流的影响研究不足的问题,提出基于无人机视频的目标时序轨迹提取方法和基于时序轨迹的行人与非机动车交通冲突识别与分析方法。以无人机监控视频作为输入,采用YOLOv5目标检测算法和DeepSORT多目标跟踪算法相结合,提取道路交叉口行人与非机动车的轨迹时序数据,经过坐标系转换,在每一时间步下,分析判断行人与非机动车间的正面冲突、追尾冲突和角度冲突。将冲突风险计算结果以热力图的形式进行可视化输出。实验结果表明,利用无人机视频提取的轨迹与实际轨迹的平均误差为7.48 cm,提出的研究方法具有较高的准确性;观测路段的交叉口处,角度冲突发生的频次最高且风险较大。研究成果能为道路交叉口数据采集提供新的思路,为路段安全风险评价提供有效的量化手段,为交通管理与控制提供决策支持。

Traffic conflict analysis based on trajectory data is an important way of evaluating road traffic flow status and safety risk.In view of the insufficient research on the impact of electric bicycles on mixed traffic flow,a method of extracting sequential trajectories of the target based on UAV video and a method of identifying and analyzing the traffic conflict between pedestrians and non-motor vehicles based on sequential trajectories are proposed.Taking UAV surveillance video as the input,the YOLOv5 target detection algorithm and DeepSORT multi-target tracking algorithm are combined to extract the sequential trajectories of pedestrians and non-motor vehicles at road intersections.At each time step,the head-on conflict,rear-end conflict and angle conflict between pedestrians and non-motor vehicles are analyzed and judged after the coordinate transformation.Then,the conflict risk calculation results are visually output in the form of a thermal diagram.The experimental results show that the average error between the trajectory data extracted by the UAV video and the actual trajectory is 7.48 cm,the proposed method has high accuracy.At the same time,the frequency of angle conflicts at the intersections of the observation section is the highest and the most severe.The research results can provide new ideas for road intersection data collection,effective quantitative means for road safety risk assessment as well as decision support for traffic management and control.