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Lane Change Maneuver Detection from Probe Vehicle DGPS Data

机译:从探头车辆DGPS数据中改变机动检测

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The impact of lane change maneuvers is fundamental to microscopic traffic flow theory. Due to the difficulty of tracking many vehicles over time and space, most of the published research in this area seeks to find lane change maneuvers visually from wayside cameras. This paper presents a different approach, finding the lane change maneuvers of a probe vehicle itself using Differential Global Positioning System (DGPS) data. We first use multiple probe vehicle trajectories through a study corridor to establish a reference trajectory from the median of all trajectories and this reference trajectory will be used to define the position of the current lane. This approach eliminates the need for high-resolution maps accurate enough to capture the exact position of the individual lanes. Our lane change maneuver detection is then divided into two parts, controlling for the impacts of mandatory lane change maneuvers (MLC) and then for discretionary lane change maneuvers (DLC). MLC are detected by comparing the difference between the mean and median of lateral distance of all trajectories relative to a reference trajectory. After distinguishing all the MLCs, the DLC are found by setting lateral thresholds around the reference trajectory, i.e., when a given trajectory leaves this virtual lane. In the process we control for the impacts of GPS errors, such as multipath, arising from obstructions. DLC are then found by comparing the out-of-threshold-line time and length to a threshold acquired empirically from data.
机译:车道变化演习的影响是微观交通流理论的基础。由于难以跟踪许多车辆的时间和空间,该领域的大多数已发布的研究都试图从路边相机视觉上寻找车道变化演习。本文呈现了一种不同的方法,使用差分全球定位系统(DGPS)数据找到探针车辆本身的车道改变操纵。我们首先通过研究走廊使用多个探头车辆轨迹来建立来自所有轨迹的中位数的参考轨迹,并且该参考轨迹将用于定义当前通道的位置。这种方法消除了对高分辨率贴图的需求足以捕获各个车道的确切位置。然后我们的车道改变机动检测分为两部分,控制强制车道改变机动(MLC)的影响,然后用于自由局部车道变化机动(DLC)。通过比较所有轨迹的横向距离相对于参考轨迹的横向距离和中值之间的差异来检测MLC。在区分所有MLC之后,通过在参考轨迹周围设置横向阈值,即,当给定的轨迹离开该虚拟通道时,找到DLC。在该过程中,我们控制来自障碍物的GPS误差(例如多径)的影响。然后通过将阈值 - 线的时间和长度与从数据获取获得的阈值进行比较来找到DLC。

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