Road roughness is the primary source of vehicle vibrations. This thesis investigates model-based methods for estimating road roughness in terms of the International Roughness Index (IRI) by measuring the chassis vibrations of the vehicle. This can provide NIRA Dynamics AB with a cost-effective pavement monitoring solution. Initially, system identification is performed on a physical car to estimate model parameters that reflect reality. Subsequently, two model-based IRI estimation methods are developed. One method relies on a transfer function between vertical chassis vibrations and the IRI according to a quarter-car model. The second method aims first to estimate the longitudinal road profile using a Kalman filter, and then calculate the IRI values from the estimated profile. This method can be implemented computationally efficiently and also offers the possibility of estimating the IRI using lateral vibrations. Both methods are validated using real-world data, and their performance is similar when using vertical vibrations, with the IRI estimation error’s standard deviation being roughly 10% to 20% of the reference value. However, the results are considerably worse when the estimation is purely based on lateral vibrations, indicating that lateral vibrations are not feasible for model-based IRI estimation, and the reasons for this are discussed.