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Evaluating Fastest Path Procedures on Roundabouts by Extracting Vehicle Trajectories from Unmanned Aerial Vehicles

机译:通过从无人驾驶飞行器中提取车辆轨迹来评估圆形交叉路口最快的路径程序

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Unmanned aerial vehicles are one of the most emerging technologies being used recently in transportation field. The collection and analysis of high-resolution data such as vehicles' trajectories are challenging concerning the analysis of driving behavior for use on optimal road design. At present, the expected speeds of vehicles on roundabouts are calculated based on fastest path analysis techniques that several guidelines provide. This paper presents the results of experimental research be conducted as a first step in developing a model for the prediction of vehicle paths on roundabouts. The research aims to define the basic path elements of vehicles maneuvers at multilane roundabouts on free-flow conditions. An analytical guiding framework regarding the use of UAVs for data acquisition in traffic analysis is presented. The proposed methodology follows a simple and low-cost way of extracting detailed naturalistic vehicle trajectories data through UAVs. Video image processing techniques and geographic information system methods are used and provide a useful tool for obtaining spatiotemporal phenomena at two multi-lane roundabouts. The level of accuracy of the method is evaluated by minimizing the errors due to camera instability and distortion effects. The results of the experiment highlight the usefulness of the proposed UAV and video processing technique for further driving behavior analysis. The analysis that was conducted indicates differences between observed vehicles trajectories and existing fastest paths procedures.
机译:无人驾驶航空公司是最近在运输领域使用的最新技术之一。车辆轨迹等高分辨率数据的收集和分析有关在最佳道路设计的驾驶行为分析的挑战。目前,基于若干指南提供的最快路径分析技术计算环形交叉路口上的预期速度。本文介绍了实验研究的结果,作为开发用于预测圆形交叉路口的车辆路径模型的第一步。该研究旨在在自由流动条件下定义MultiLane环形交叉路口的车辆行动的基本路径元素。介绍了关于在流量分析中使用无人机使用无人机的分析指导框架。所提出的方法介绍了通过无人机提取详细的自然主义车辆轨迹数据的简单而低成本的方式。使用视频图像处理技术和地理信息系统方法,并提供一个有用的工具,用于在两个多车道环形交叉路口获得时空现象。通过最小化由于相机不稳定性和失真效果,通过最小化误差来评估方法的准确性水平。实验结果突出了所提出的UAV和视频处理技术的有用性,以便进一步驾驶行为分析。进行的分析表明观察到的车辆轨迹与现有最快的路径程序之间的差异。

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