Estimation of Road Roughness Based on Tire Pressure Monitoring

The evaluation of road roughness plays a critical role in the life-long maintenance of the highway system. This study proposes a Kalman Filter-based scheme to evaluate the road roughness indirectly from the response of a moving adapted monitoring vehicle. Key feature of the scheme is the use of measurements from dynamic tire pressure of unsprung mass components that directly interact with roads. Combination of ideal gas law and elastic contact model results in a nonlinear relationship between the tire pressure and the contact force, in which the parameters are calibrated by the Extended Kalman Filter. Identification of vehicle's physical parameters adopts the power spectrum method with a known-size bump test. Subsequently, the road roughness is treated as unknowns in the vehicle's state-space equation and solved by the Discrete Kalman Filter with unknown inputs. The estimated road roughness profiles are then used to calculate the International Roughness Index and compared with that provided by the standardized laser profilometer, an outer-systematic comparison. On the other hand, available measurements are split into groups that measurements of tire pressure are used to predict the accelerations of the car body and wheels and compared with these accelerations directly measured from accelerometers, an inner-systemic comparison. Field tests are carried out on a 900 m long standardized road under two scenarios of with and without the bump and four different vehicle running speeds from 20 to 50 km/h. Consistence of comparison from different perspectives proves the reliability of the proposed scheme. In addition, the results unveil that the scenario with a lower running speed can offer a better estimation of road roughness.

Automated summary

This study proposes a Kalman Filter-based scheme to indirectly evaluate road roughness using measurements of tire pressure. The scheme uses Extended Kalman Filter to calibrate parameters and solves unknowns in the vehicle's state-space equation using Discrete Kalman Filter. The results demonstrate the reliability of the proposed scheme and reveal the potential for better estimation of road roughness at lower running speeds.