IRI Estimation by the Frequency Domain Analysis of Vehicle Dynamic Responses and Its Large-scale Application
IRI Estimation by the Frequency Domain Analysis of Vehicle Dynamic Responses and Its Large-scale Application
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DOI：
10.1145/3004010.3004013
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Boyu Zhao;T. Nagayama;N. Makihata;Masashi Toyoda;Muneaki Takahashi;Masataka Ieiri
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文献类型：
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作者：
Boyu Zhao;T. Nagayama;N. Makihata;Masashi Toyoda;Muneaki Takahashi;Masataka Ieiri
为了高效、准确地实现大规模道路评估，开发了基于智能手机的动态响应智能监测系统（iDRIMS）。iDRIMS根据iOS应用程序（iDRIMS测量）测量的普通车辆的动态响应，获得三轴加速度、角速度和精确采样定时的GPS，根据国际粗糙度指数（IRI）对道路状况进行评估。然而，该方法的鲁棒性和准确性有限。本文主要利用频域分析对iDRIMS进行改进。改进的IRI估计算法包括两个步骤。首先，选取能够再现车辆弹跳和俯仰运动并代表传感器纵向安装位置的半车（Half-Car， HC）模型作为车辆数值模型并进行识别。通过在已知尺寸的便携式驼峰上进行驾驶试验，确定了车辆参数。与之前使用卡尔曼滤波在时域识别参数的方法不同，该方法使用遗传算法（GA）对参数进行优化，以最大限度地减少频域模拟与实测驼峰响应之间的差异。然后，通过测量普通车辆的垂直加速度响应来估计IRI。通过乘以传递函数，将测量到的加速度转换为标准四分之一车簧载质量的加速度均方根。与之前的QC模型相比，通过对识别出的HC模型进行仿真来估计传递函数，传递函数反映了车辆俯仰运动和传感器安装位置。基于这些值之间的相关性，RMS进一步转换为IRI。通过数值模拟研究了不同驱动速度和传感器位置下的性能。实验在13km的道路上进行，通过比较三种类型的车辆和剖面仪。此外，改进的方法应用于大约70辆商用车，这些商用车每年行驶超过18万公里。搭建了数据采集分析平台，成功高效地采集和分析了大量数据。数值模拟和实际应用结果表明，改进后的方法能够准确估计IRI，具有较高的鲁棒性和效率。
In order to achieve large scale road evaluation with high efficiency and accuracy, smartphone based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed [4]. iDRIMS evaluates road condition in terms of International Roughness Index (IRI) based on dynamic responses of ordinary vehicles measured with an iOS application (iDRIMS measurement), which obtains three axis acceleration, angular velocity and GPS with accurate sampling timings. However, the robustness and accuracy was limited. In this paper, iDRIMS is improved mainly by employing the frequency domain analysis. The improved algorithm for IRI estimation consists of two steps. At first, a Half-Car (HC) model, which can reproduce both vehicle bouncing and pitching motions and represent sensor installation location in the longitudinal direction, is selected as the vehicle numerical model and identified. The vehicle parameters are identified through a drive tests over a portable hump with a known size. As opposed to previous approach of parameter identification in the time domain using Kalman filter, the parameters are optimized to minimize the difference between simulation and measured hump responses in the frequency domain using Genetic algorithm (GA). Then, IRI is estimated by measuring vertical acceleration responses of ordinary vehicles. Measured acceleration is converted to the acceleration RMS of the sprung mass of standard quarter car by multiplying a transfer function. The transfer function, estimated through the simulation of the identified HC model as opposed to QC model in previous approaches, reflects the vehicle pitching motions and sensor installation location. The RMS is further converted to IRI based on correlation between these values. Numerical simulation is conducted to investigate the performance in terms of various drive speeds and sensor locations. Experiment is carried out at a 13km road by comparing three types of vehicles and profiler. Furthermore, the improved method is applied to about 70 commercial vehicles, which drive over more than 180,000 km per year. Data collection and analysis platform is built, which successfully collected and analyzed large-scale data with high efficiency. Results from both numerical simulation and real case application indicate that the improved method can accurately estimate IRI with high robustness and efficiency.