###### Abstract  
We investigated the stochastic response of a person sitting in a driving vehicle to quantify the impact of an uncertain parameter important in controlling defect reduction in terms of ride comfort. Using CarSim software and MATLAB/Simulink, we developed a fully coupled model that simulates a driving vehicle combined with an analytical nonlinear human model. Ride comfort was evaluated as a ride index considering the frequency weights defined in BS 6841. Additionally, to investigate the uncertainty of the ride index, a framework for calculating the ride index was proposed using the generalized polynomial (gPC) method. Further, sensitivity analysis of the ride index was performed for each uncertainty parameter, such as stiffness and damping. The results obtained through the gPC method were in good agreement with those obtained via Monte Carlo simulation (MCS) and were excellent in terms of computation time without a loss of numerical accuracy. Through in-depth investigation, we found that the stochastic distribution of the ride index varies differently for each uncertain parameter in the human model. By comparing linear and nonlinear human models, we also found that the nonlinearity of the human model is an important concern in the stochastic estimation of ride comfort. human-vehicle model; uncertainty; ride index; generalized polynomial chaos; stochastic analysis +
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hybrid vehicles has led to a new turning point in the study of vibration analysis in the automotive industry. In general, these vehicles do not mask road-induced vibrations in the low-frequency range owing to the absence of a combustion engine [13]. Therefore, understanding human responses to vibrations will become even more important in the near future. Because experimental approaches for the evaluation of ride comfort are time-consuming and costly, increasing attention is being paid to the industrial requirements for computational analysis based on dynamic simulations. [PERSON] et al. [14] developed a model that combines a seat and a human as well as a five-degrees-of-freedom mathematical model, computed weighted accelerations, and a Seat Effective Amplitude Transmissibility (SEAT) index. Additionally, [PERSON] et al. [15; 16] introduced a biomechanical human model obtained using a computational multibody system and investigated the ride comfort index with respect to road roughness and the longitudinal velocity of a vehicle. [PERSON] et al. [17] adopted a human-vehicle model with 14 degrees of freedom to analyze the ride comfort of the proposed active suspension vehicle system. [PERSON] et al. [18; 19] developed a train-seat-human model with vertical, lateral, and roll vibrations to study the ride comfort of rail vehicles. In general, the parameters of a real human body (e.g., weight, length, and stiffness) are highly uncertain compared to typical mechanical structures [20; 21]. Furthermore, human parameters result in time-variant systems because of the variations in muscle tension, blood flow, and other environmental circumstances. Consequently, the dynamic response of the human body to whole-body vibrations is uncertain due to the uncertainty of the parameters. Therefore, numerical calculations of dynamic human responses to whole-body vibrations using deterministic parameters are limited in the sense that they do not provide a unique subjective rating of feeling. Numerous stochastic methods have been introduced to calculate the response of a system in the presence of parameters with uncertainty. Among them, Monte Carlo simulation (MCS) is widely used to estimate system uncertainty [22; 23; 24; 25; 26]. In MCS, a set of samples of an input parameter with uncertainty are randomly selected from the corresponding probability distributions that are estimated or assumed to be given, and multiple simulations are taken to compute the corresponding output for each set of samples. However, due to the nature of MSC and random sampling, a huge number of simulations are required, which can be computationally time-consuming.