Estimating Vehicle Mass and Road Grade through Bayesian Inversion. Issue 10 (2021)

• Record Type: Journal Article
• Authors: Shen, Xun; Zhang, Yahui
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 10(2021)
• Page Start: 235
• Page End: 240
• Publication Date: 2021
• Subjects: Parameter estimation -- Bayesian inversion -- least squares -- statistical analysis; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.10.169
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Vehicle loads such as those due to vehicle mass and road grade need to be determined from measurements. However, due to the uncertainties of measurements, the corresponding estimations are uncertain as well. This paper addresses parallel vehicle mass and road grade estimation problems with a Bayesian inversion-based approach, intending to give estimations in a statistical sense. The parallel estimation problem is firstly reformulated as a statistical inverse problem. Then, the assumption is made on the prior probability distribution of vehicle mass and road grade. The posterior is updated by feeding measured data and the best estimation is determined according to the obtained posterior distribution. Comprehensive validation of the estimation performance is conducted statistically for the proposed method with a comparison with least squares approach. The results indicate that the Bayesian inversion approach gives a parallel estimation with higher statistical reliability than least squares method.