IRI Estimation by the Frequency Domain Analysis of Vehicle Dynamic Responses and Its Large-scale Application

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.