College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250399, China.
Sensors (Basel). 2023 Jan 30;23(3):1515. doi: 10.3390/s23031515.
Concerning roadside traffic detection applications, and to address the millimeter-wave radar's missing data problem caused by target occlusion or the absence of features in low-speed conditions, this paper proposes a trajectory compensation method regarding car-following behavior. Referring to the installation scheme of the detector, a coordinate transformation method is presented to unify the radar spatial coordinates with the road coordinates. Considering the driver's car-following behavior, the optimal velocity model (OV), full velocity difference model (FVD), and the full velocity difference and acceleration (FVDA) model are applied for tracking the vehicle's trajectory related to the movement of the vehicle ahead. Finally, a data compensation scheme is presented. Taking actual trajectory data as samples, the proposed methods are verifiably useful for compensating for missing data and reconstructing target trajectories. Statistical results of different missing data trajectories demonstrate the rationality of the application of car-following models for the missing data compensation, and the FVDA model performs well compared with the OV and FVD models.
针对路边交通检测应用,并为了解决毫米波雷达在目标被遮挡或低速条件下无特征时的缺失数据问题,本文提出了一种基于跟车行为的轨迹补偿方法。参考探测器的安装方案,提出了一种坐标变换方法,将雷达空间坐标与道路坐标统一起来。考虑到驾驶员的跟车行为,应用了最优速度模型(OV)、全速度差模型(FVD)和全速度差及加速度模型(FVDA)来跟踪与前车运动相关的车辆轨迹。最后,提出了一种数据补偿方案。以实际轨迹数据作为样本,验证了所提出的方法在补偿缺失数据和重构目标轨迹方面的有效性。不同缺失数据轨迹的统计结果表明,跟车模型在缺失数据补偿中的应用具有合理性,并且 FVDA 模型的性能优于 OV 和 FVD 模型。