System Identification of Vehicle Dynamics and Road Conditions Using Wireless Sensors

Abstract

Road quality and ride comfort are major concerns when creating and maintaining roads. Ride comfort is dependent on the interaction between the vehicle and the roughness of the road. Road roughness is currently measured by road-profiling vehicles in a quantifiable term known as the International Roughness Index (IRI). Although this method is useful for determining road surface information, it is a time consuming process, it can't be carried out every day, and it does not provide a direct indication of ride comfort. However, advancements in sensor technology provide necessary enhancements that current methods cannot address. This study aims to develop an innovative method using built-in wireless sensing and mobile computing features of smartphones to not only estimate road roughness, but to provide a direct real-time indication of ride comfort.

Estimation of road roughness based on vehicle response involves insight regarding the properties of the vehicle itself. While the vibration response of the vehicle can be readily measured using wireless accelerometers or built-in smartphone sensors, information pertaining to the vehicle and road properties is left unknown. To address this issue, various system identification methods are evaluated for high-damped systems and applied to the vehicle. Through the application of system identification methods using vehicle response data, the unknown parameters of the vehicle can be estimated. These methods are validated through analysis of vehicle model simulation paired with standard simulated road profiles. Furthermore, these simulations create an environment to determine optimal conditions for vehicle mass prediction. With vehicle parameters identified, the dynamic response parameters of the vehicle and the input of the road surface profile can be correlated to estimate the IRI while directly providing ride comfort information. Field testing involving the use of a wireless accelerometer and GPS is also implemented to compare recorded data against the simulation findings.

This study establishes a framework that integrates wireless sensors, system identification methods, and the correlation between ride comfort and the IRI with vehicle vibration measurements. System identification methods with a focus on vehicles subjected to excitation from the road are evaluated. This involves an investigation of prediction error identification methods with the use of grey-box modeling to estimate vehicle mass under varying road conditions. With vehicle parameters known, correlation of vehicle vibration response with the IRI and ride comfort is empirically established to determine areas of road in need of maintenance along with comfortable travel routes in real-time. This study demonstrates the appeal for including information related to road conditions and ride comfort in mobile maps for alternative travel routes.