Validation in vehicle engineering identifies and quantifies the differences between simulation models and experiment data. In this work we consider these differencesthe lack of ability to model uncertainties and to identify unknown parameters values, especially for coupled complex systems such as vehicles. Effects of unknown model parameters vary under different maneuvers and the ability to excite a source of uncertainty is the focus of this study. We propose an optimization method to generate a proper maneuver that maximize the sensitivity of uncertain parameters based on global sensitivity analysis (GSA). Sensitivities with respect to individual uncertain parameters and those that consider coupled effects are all included. We utilize Kriging-based metamodels to improve the efficiency of the GSA problems with computationally expensive simulations. The optimal design of excitation maneuvers to create the most sensitive performances can then be obtained. The applicability and the accuracy of the proposed method are assessed via a math model and a practical application on a x-by-wire autonomous tricycle. Results show that our proposed method can assist in providing a suitable maneuver as an alternative validation to uncertain parameters in a vehicle system.