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基于实时通信条件下的车辆轨迹重构的信号交叉口左转冲突识别。

Left-turn conflict identification at signal intersections based on vehicle trajectory reconstruction under real-time communication conditions.

机构信息

School of Transportation Science and Technology, Harbin Institute of Technology, 150020, Harbin, China.

School of Transportation Science and Technology, Harbin Institute of Technology, 150020, Harbin, China.

出版信息

Accid Anal Prev. 2021 Feb;150:105933. doi: 10.1016/j.aap.2020.105933. Epub 2020 Dec 17.

Abstract

Connected vehicle (CV)technologies offer promising solutions to several problems in transportation systems. The trajectory data generated from CV technology can be used to identify real-time conflicts in intersections. To perform such identification, accurate vehicle localisation should be obtained to clearly recognise the conflicts between left-turning vehicles and straight-through vehicles in the opposite direction at the signal control intersection. This study presents a CV framework that uses the two-way time of arrival to locate the vehicles on the basis of the Intelligent Vehicle Infrastructure Cooperative Environment. Kalman Filter (KF) is used to improve the accuracy of the vehicle location, and the corresponding algorithm is used to estimate the vehicle trajectory to obtain the vehicle kinematics information via the on-board system. The traffic conflict areas of the left-turning vehicles and straight-through vehicles in the opposite direction are determined through vehicle trajectory extrapolation, and the left-turn collision at the signal intersection is identified using the post-encroachment time algorithm and vehicle movement information. In addition, Anderson-Darling and modified Kolmogorov-Smirnov tests are performed to verify the goodness of fit of the data. Results show that the vehicle speed and localisation errors of the proposed method decreased by 66.67 % and 83.33 % compared with the results before filtering, respectively. Moreover, the results of the conflict recognition method based on CV trajectory reconstruction is consistent for both goodness of fit tests under real-time communication conditions. This study can provide driving decision for drivers of left-turning vehicles under the Intelligent Vehicle Infrastructure Cooperative Environment and provide technical support for the research and development of left-turn anti-collision systems.

摘要

车联网 (CV) 技术为交通系统中的多个问题提供了有前景的解决方案。CV 技术生成的轨迹数据可用于识别交叉口的实时冲突。为了进行这种识别,应该获得准确的车辆定位,以清楚地识别信号控制交叉口处对向左转车辆与直行车之间的冲突。本研究提出了一种基于智能车辆基础设施合作环境使用双向到达时间定位车辆的 CV 框架。卡尔曼滤波 (KF) 用于提高车辆位置的准确性,并使用相应的算法通过车载系统估计车辆轨迹以获得车辆运动学信息。通过车辆轨迹外推确定对向左转车辆和直行车的交通冲突区域,并使用后侵入时间算法和车辆运动信息识别信号交叉口的左转碰撞。此外,还进行了安德森-达林和修正的柯尔莫哥洛夫-斯米尔诺夫检验,以验证数据的拟合优度。结果表明,与滤波前的结果相比,所提出的方法的车辆速度和定位误差分别降低了 66.67%和 83.33%。此外,基于 CV 轨迹重建的冲突识别方法的结果在实时通信条件下的拟合优度测试下都是一致的。本研究可以为智能车辆基础设施合作环境下的左转车辆驾驶员提供驾驶决策,并为左转防撞系统的研究和开发提供技术支持。

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