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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >A Fixed Sensor-Based Intersection Collision Warning System in Vulnerable Line-of-Sight and/or Traffic-Violation-Prone Environment
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A Fixed Sensor-Based Intersection Collision Warning System in Vulnerable Line-of-Sight and/or Traffic-Violation-Prone Environment

机译:弱视线和/或交通违规标杆环境中基于传感器的固定交叉口碰撞预警系统

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摘要

This paper proposes a cooperative intersection collision warning system (CICWS) model that uses fixed traffic sensors to provide warning information to drivers at unsignalized intersections in a real-time manner. The CICWS model is useful for vulnerable line-of-sight and/or traffic-violation-prone environment since it determines whether the situation is really dangerous or not. More specifically, the model is for unsignalized intersections without STOP/YIELD signs, where drivers do not tend to stop. The situation forecast model uses vehicle location, speed, and time data obtained from multiple sensors located at intersection approaches, together with obstacle position and sight distance relationship. More specifically, the model has a real-time sight-distance triangle module and a collision-time prediction module. Using a microtraffic simulator called VISSIM, the validation and evaluation of the model are performed based on different scenarios with different parameters, such as inflow volume, locations of traffic sensors, design speed, and obstacle placement. The results show that the model successfully forecasts dangerous situations up to 94.3%, which may imply the deployment of the model in such an environment where vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) communication are possible. Some limitations and a future research agenda have also been discussed.
机译:本文提出了一种合作式交叉路口碰撞预警系统(CICWS)模型,该模型使用固定的交通传感器向未信号交叉口的驾驶员实时提供警告信息。 CICWS模型对于易受攻击的视线和/或易于违反流量的环境很有用,因为它可以确定情况是否真的很危险。更具体地说,该模型适用于没有STOP / YIELD标志的无信号交叉路口,在这些交叉路口驾驶员不会停车。情况预测模型使用从位于交叉路口进近的多个传感器获得的车辆位置,速度和时间数据,以及障碍物位置和视距关系。更具体地说,该模型具有实时视距三角形模块和碰撞时间预测模块。使用称为VISSIM的微交通仿真器,可以基于具有不同参数(例如流入量,交通传感器的位置,设计速度和障碍物放置)的不同场景来执行模型的验证和评估。结果表明,该模型成功地预测了高达94.3%的危险情况,这可能意味着该模型在可能进行车对基础设施(V2I)或车对车(V2V)通信的环境中的部署。还讨论了一些局限性和未来的研究议程。

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