MATLAB-Based Road Roughness Simulation Program

Road roughness simulation is a critical component in vehicle dynamics research, directly impacting vibration analysis and suspension system design. MATLAB serves as an ideal platform for such simulations due to its powerful mathematical computing capabilities and advanced visualization tools. The simulation program typically follows international standards (such as ISO 8608) that define road roughness Power Spectral Density (PSD) models. The core methodology involves generating road elevation data matching specific classification grades (A-E) through stochastic processes. Key implementation steps include: White noise generation - Using MATLAB's randn() function to create Gaussian-distributed random sequences as base input signals. This provides the fundamental stochastic component for roughness modeling. Frequency filtering - Applying amplitude modulation and phase processing to white noise based on target road surface spatial frequency characteristics. This can be implemented using filter design functions like butter() for Butterworth filters or fir1() for FIR filters to match standard PSD profiles. Inverse Fourier Transform - Converting frequency-domain signals to time/space domain road elevation data through ifft() function. This transformation creates realistic road profile data from spectral specifications. Visualization output - Displaying 2D road profiles using plot() function or 3D terrain maps using surf() and mesh() functions. Additional visualization enhancements can include contour plots and animated vehicle responses. In extended applications, the program can be integrated with Simulink for co-simulation to evaluate vehicle dynamic responses across different road grades. It can also be combined with machine learning algorithms for road classification research. Potential optimization directions include incorporating non-Gaussian characteristics, local obstacle modeling, and other practical complex factors to enhance simulation realism.