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The pavement of a road that is subject to deterioration due to vehicle loads needs quantitative and frequent evaluation. The road profile, which determines the ride quality, is an important property, though its estimation is usually costly and thus, infrequent. In this study, a road profile estimation method using an ordinary-vehicle's responses measured by only a smartphone is developed. The algorithm consists of two steps. At first, an ordinary vehicle is modelled as a half car (HC) and a genetic algorithm (GA) identifies its parameters by using the responses of the vehicle passing over a known-size hump. With the estimated vehicle model, an augmented Kalman filter, in which the road profile is included in the state vector, estimates the road profile; Rauch-Tung-Streiber (RTS) smoothing is employed to improve the accuracy. The observation variables and locations are determined based on an observability analysis. A numerical simulation is conducted to investigate the profile estimation performance in terms of drive speeds, model error, and measurement noise. The experiment is carried out on a 13 km road. The profiles estimated by three types of ordinary vehicles are compared with a reference profile obtained by a laser profiler to validate the proposed method. Results from both the simulation and the experiment show that the combination of the vehicle parameter estimation and the profile estimation methods accurately estimates the road profile with a high degree of accuracy and robustness. • Response-based road profile estimation using a smartphone on a vehicle is proposed. • A pitching HC model with 10 unknowns is calibrated when it passes over a hump. • The difference in dynamic properties, sensor locations, and speeds are compensated. • The accuracy of estimation is confirmed using a high-accuracy laser profiler. [ABSTRACT FROM AUTHOR]

The aim of this work is to present a novel accurate estimator for vehicle dynamics. Following the multibody dynamics approach, a vehicle can be modeled with a high level of detail including non-linear dynamics. As a consequence, a rich simulation data-set is available for engineering analysis, richer than with vehicle analytical models. The proposed novel estimator is a new form of a dual Kalman filter. The first filter uses an indirect extended Kalman filter (i.e. the errorEKF) incorporating force estimation and using a vehicle multibody model. The second filter is an unscented Kalman filter (UKF) used to increase the accuracy of the errorEKF by estimating uncertain modeling parameters such as the mass of the vehicle and the tire-road friction coefficient. The performance of the proposed state-parameter-input (SPI) observer is tested in a simulation environment. The performance of the observer is demonstrated using two maneuvers, out of which one covers aggressive driving conditions. The results show that the new observer estimates with high accuracy the variables of interest for vehicle dynamics, such as the tire forces. [ABSTRACT FROM AUTHOR]