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Estimating Vehicle Mass and Road Grade through Bayesian Inversion

机译：通过贝叶斯反演估算车辆质量和道路等级

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摘要

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.
机译：诸如车辆质量和道路等级的车辆载荷需要从测量中确定。然而，由于测量的不确定性，相应的估计也不确定。本文满足了基于贝叶斯反演的方法的平行车辆质量和道路级估计问题，打算在统计学中提供估计。并行估计问题首先重新重新重新重整为统计逆问题。然后，假设是车辆质量和道路等级的现有概率分布。通过馈送测量数据更新后部，并且根据所获得的后部分布确定最佳估计。估计性能的综合验证是针对最小二乘方法进行比较的提出的方法进行统计进行。结果表明，贝叶斯反转方法具有比最小二乘法的统计可靠性更高的平行估计。

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