基于地空信息融合的无人机交通状态感知方法研究

现有的无人机(UAV)交通状态感知方法,主要针对宏观交通状态参数的获取,同时尚未克服UAV自运动对交通参数检测精度的影响,难以满足智能交通系统对于高精度微观交通参数的应用需求.为此,提出一种基于地空信息融合的UAV交通状态感知方法,该方法包括:地空信息融合模型、道路关键点(IKP)检测及跟踪、车辆目标检测及追踪算法和交...

Research on UAV Traffic State Perception Method Based on Air-ground Information Fusion

The existing UAV (Unmanned Aerial Vehicle) traffic state perception method is mainly aimed at the acquisition of macroscopic traffic state parameters and has not overcome the impact of UAV self-motion on the detection accuracy of traffic parameters. It is difficult to meet the application requirements of intelligent transportation systems for high-precision microscopic traffic parameters. To this end, a UAV traffic state perception method based on air-ground information fusion is proposed. The method includes air-ground information fusion model, infrastructure key points (IKPs) detection and tracking, vehicle detection and tracking algorithm, and traffic state parameter extraction and estimation. The air-ground information fusion model uses ground-based information (IKP world coordinates) and air-based information (IKP pixel coordinates) for optimal fusion and updates the real-time parameters through the adaptive IKP tracking and offset estimating algorithm. This overcomes the influence of UAV self-motion on the accuracy of vehicle trajectory and obtains reliable vehicle-level (instantaneous speed, headway, and headway time) and lane-level (lane dynamic density, lane flow, and space average vehicle speed) traffic state parameters. The proposed perception method is used to obtain and validate the distribution of the vehicle-level traffic parameters of the field video. At the same time, the estimation methods of lane-level parameters based on different traffic flow models are compared. The results show that the mAP@0.5 index of vehicle detection exceeds 90%, the extracted vehicle trajectories are relatively intact, and the obtained vehicle-level and lane-level traffic state parameters conform with the actual traffic flow conditions. Finally, the model is applied to the traffic jam and incident detection. The research results provide theoretical and technical support for the application of UAV in modern traffic perception and management.