摘要
车辆轨迹数据因其丰富的细节而成为交通运输研究的热点。车道信息是轨迹数据的一个重要方面，通常使用能够提取道路车道特征的摄像机或激光雷达等传感器获得。然而，一些用于轨迹跟踪的传感器(例如，毫米波雷达传感器)无法提供车道信息。基于这些传感技术的车辆检测和轨迹跟踪系统可以在初始安装期间通过手动校准与车道信息整合，但这一过程是劳动密集型的，并且随着传感器逐渐被风和振动偏离，需要频繁重新校准。这对轨迹跟踪，特别是实时应用提出了挑战。针对这一挑战，本文提出了一种利用微观轨迹数据估计车道水平道路几何形状的方法。该方法包括使用方向矢量分割轨迹点，并对其进行聚类，并拟合一系列聚类中心点。利用估计结果与地面真值参考距离的平均误差(ME)来衡量不同条件下车道水平道路几何参数估计的精度。结果表明，当平均轨迹数据在每段至少包含约30个每米点时，ME始终小于0.1m。文中还对毫米波雷达数据进行了测试，验证了该方法的有效性。验证了该方法在车辆轨迹跟踪系统中道路线形动态标定的可行性。
Abstract
Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.
NETL
NSTL
Wiley-Hindawi