Multi-Sensor Fusion Based Estimation of Tire-Road Peak Adhesion Coefficient Considering Model Uncertainty

Abstract

The tire-road peak adhesion coefficient (TRPAC), which cannot be directly measured by on-board sensors, is essential to road traffic safety. Reliable TRPAC estimation can not only serve the vehicle active safety system, but also benefit the safety of other traffic participants. In this paper, a TRPAC fusion estimation method considering model uncertainty is proposed. Based on virtual sensing theory, an image-based fusion estimator considering the uncertainty of the deep-learning model and the kinematic model is designed to realize the accurate classification of the road surface condition on which the vehicle will travel in the future. Then, a dynamics-image-based fusion estimator considering the uncertainty of visual information is proposed based on gain scheduling theory. The results of simulation and real vehicle experiments show that the proposed fusion estimation method can make full use of multisource sensor information, and has significant advantages in estimation accuracy, convergence speed and estimation robustness compared with other single-source-based estimators.

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

“…However, such a method based on the response recognition of tires is affected by many external uncertainties due to the complexity of the generation mechanism of the tire noise, and sometimes it is insurmountable to accurately identify the adhesion coefficients. Recently, some identification methods on the basis of visual information have been proposed in the current study [ 25 , 26 , 27 , 28 ]. For example, given the nondeterminacy of kinematic models and deep-learning models, an image-based fusion estimation method by virtue of the virtual sensing theory was put forward to exactly realize the identification of the road surface condition in reference [ 25 ].…”

“…Recently, some identification methods on the basis of visual information have been proposed in the current study [ 25 , 26 , 27 , 28 ]. For example, given the nondeterminacy of kinematic models and deep-learning models, an image-based fusion estimation method by virtue of the virtual sensing theory was put forward to exactly realize the identification of the road surface condition in reference [ 25 ]. However, these visual information-based methods are susceptible to light.…”

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

“…In sensor networks, the problem of estimating the state observed by multiple sensors has been analyzed extensively in recent decades due to the variety of applications they have in signal processing (see, e.g., [1][2][3][4][5][6][7][8][9]).…”