Successful research in vehicle dynamics is founded on strict mathematical and physical laws, which must be organized within a coherent analytical framework. This framework incorporates all the necessary methods and techniques required for both current and future research activities. The stochastic nature of experimental data, along with the digitalization of input and output functions, highlights the importance of accurate identification of all technical parameters. A precise problem formulation and explicit parameter identification enable the designed mathematical model to accurately calculate the expected results. The development of an accurate vehicle dynamics model, based on a well-defined symbolic equation of motion, depends on the correct transformation of measured signals and the application of methods from stochastic process dynamics. Moreover, real-time parameter identification plays a crucial role in the comprehensive characterization of system parameters. Within this approach, the magnitudes and interrelations of parameters are evaluated using power spectral density, autocorrelation, and covariance functions.