Automatic Traffic Safety Analysis using Unmanned Aerial Vehicle Technology at Unsignalized Intersections in Heterogeneous Traffic

Debashis Ray Sarkar, K. Ramachandra Rao, Niladri Chatterjee
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Abstract

A generalized, reliable unmanned aerial vehicle (UAV) system for visual tracking and detection of road vehicles from aerial videography would outperform traditional traffic monitoring systems, providing extensive coverage and optimal study area perspectives. The combination of UAV technology for data collection and advanced video processing tools for visual tracking would assist traffic engineers in a detailed spatial and temporal utilization analysis with accurate traffic characteristics. Initially, traffic conflicts were determined by post encroachment time from visual data at unsignalized intersection. But a new concept (known as “required post encroachment time”) has been proposed to differentiate between critical and non-critical conflicts among road users. Finally, by extracting the information of vehicle trajectories, we have also developed a “collision probability evaluation model” to determine the severity level of critical conflicts in heterogeneous traffic conditions. Our numerical results show the high precision of our suggested model with regard to risk recognition when evaluating the collision probability at the study intersection. This research utilizes vehicle trajectories to evaluate driving risk at intersections through automatic traffic safety analysis.
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利用无人驾驶飞行器技术对异质交通中的无信号交叉口进行自动交通安全分析
通过空中摄像对道路车辆进行视觉跟踪和检测的通用、可靠的无人驾驶飞行器(UAV)系统将优于传统的交通监控系统,可提供广泛的覆盖范围和最佳的研究区域视角。将用于数据收集的无人机技术与用于视觉跟踪的先进视频处理工具相结合,将有助于交通工程师利用准确的交通特征进行详细的空间和时间利用分析。最初,交通冲突是通过无信号灯交叉口的可视数据的后侵占时间来确定的。但有人提出了一个新的概念(称为 "所需的侵占后时间"),以区分道路使用者之间的关键冲突和非关键冲突。最后,通过提取车辆轨迹信息,我们还开发了一种 "碰撞概率评估模型",用于确定异构交通条件下严重冲突的严重程度。我们的数值结果表明,在评估研究交叉口的碰撞概率时,我们建议的模型在风险识别方面具有很高的精确度。这项研究通过自动交通安全分析,利用车辆轨迹来评估交叉路口的驾驶风险。
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