Road traffic accidents, especially vehicle pedestrian collisions in crosswalk, globally pose a severe threat to human lives and have become a leading cause of premature deaths. In order to protect such vulnerable road users from collisions, it is necessary to recognize possible conflict in advance and warn to road users, not post facto. A breakthrough for proactively preventing pedestrian collisions is to recognize pedestrian's potential risks based on vision sensors such as CCTVs. In this study, we propose a predictive collision risk area estimation system at unsignalized crosswalks. The proposed system applied trajectories of vehicles and pedestrians from video footage after preprocessing, and then predicted their trajectories by using deep LSTM networks. With use of predicted trajectories, this system can infer collision risk areas statistically, further severity of levels is divided as danger, warning, and relative safe. In order to validate the feasibility and applicability of the proposed system, we applied it and assess the severity of potential risks in two unsignalized spots in Osan city, Korea.

中文翻译：

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城市监控基础设施行人主动预防系统中预测碰撞危险区域估计的新方法

道路交通事故，特别是人行横道中的行人碰撞，在全球范围内严重威胁着人类的生命，并已成为导致过早死亡的主要原因。为了保护这种脆弱的道路使用者免于碰撞，有必要事先识别可能的冲突并警告道路使用者，而不是事后警告。主动预防行人碰撞的突破是基于视觉传感器（如CCTV）识别行人的潜在风险。在这项研究中，我们提出了在无信号人行横道上的预测碰撞风险区域估计系统。拟议的系统对经过预处理的视频片段中的车辆和行人的轨迹进行了预处理，然后使用深度LSTM网络预测了它们的轨迹。利用预测的轨迹，该系统可以统计推断碰撞危险区域，级别的进一步严重程度分为危险，警告和相对安全。为了验证该系统的可行性和适用性，我们对其进行了应用并评估了韩国乌山市​​两个未信号化地点的潜在风险的严重性。