Zhang Cheng, Shen Shihui, Huang Hai, Wang Linbing
Department of Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USA.
Rail Transportation Engineering, Penn State Altoona, Altoona, PA 16601, USA.
Sensors (Basel). 2021 Mar 2;21(5):1721. doi: 10.3390/s21051721.
Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.
交通信息对于路面设计、管理和健康监测至关重要。已经开发并安装了许多路面传感器来收集交通流量和荷载幅值。然而，对车速估计算法的关注有限。本研究聚焦于基于互相关方法的车速估计。一种新型无线微机电传感器（MEMS），即智能岩石，被用于采集三轴加速度、旋转和应力数据。互相关算法，即归一化互相关（NCC）算法、平滑相干变换（SCOT）算法和相位变换（PHAT）算法，被应用于估计加速路面试验（APT）的加载速度和现场的交通速度。利用信噪比（SNR）和平均相对误差（MRE）来评估算法的稳定性和准确性。结果表明，相关噪声和独立噪声对现场数据都有显著影响。由于在各算法中具有较大的SNR值和最低的MRE值，SCOT算法因具有合理的准确性和稳定性而被推荐用于速度估计。本研究中所研究的加载速度在50 km/h以内，对于更高速度的估计还需要进一步验证。
