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• Developing a complex vehicle model including tire, suspension, and firing system. • Uncertainty definition for significant parameters selected by analysis of variance. • Constructing the surrogate models using an effective adaptive sampling method. • Prediction of uncertainty propagation by using surrogate model-based Monte Carlo simulation. • Obtaining a joint probability of correctness for integrated vehicle performance. The design uncertainties of vehicles cause variation of the vehicle performance. This variation increases with the complexity of the vehicle; e.g., it is greater for heavy-duty vehicles than for passenger cars. This paper presents an efficient uncertainty quantification method based on uncertainty definition, propagation, and certification, with regard to the integrated performance of a heavy-duty vehicle. For the uncertainty definition of the design parameters, an analysis of variance is performed to select the parameters with the greatest effect on the performance, and various probability density functions are employed for these parameters. To predict the precise uncertainty propagation of the vehicle performance and reflect the design uncertainty in the real-world, a full vehicle model is constructed. Additionally, a Monte Carlo simulation (MCS) with surrogate models is performed to assess the efficiency and accuracy of the performance estimation. To efficiently develop the surrogate models, an adaptive-sampling method is used to reduce the required amount of sampling data. For certification of the required performance, the joint probability of correctness for the integrated performance is suggested for practical application, and a comparison of the probability results between the surrogate and dynamic vehicle models indicates the accuracy of MCS with a surrogate model. [ABSTRACT FROM AUTHOR]