In the past years, automated driving has become one of the most important research fields in the automotive industry. A key component for a successful substitution of human driving by vehicles is a real-time model of the current environment including the traffic situation, the guide-way, and the road itself. Although, most of the information for the environment model are provided via in-vehicle generated data based on camera, LIDAR, and RADAR sensors, we propose a solution of classifying road quality within the spring-damper system of the vehicle. In this paper, we utilize the Vehicle Level Sensor (VLS), which is a standard component in modern vehicles, for road condition assessment. We present a simulation of the Quarter Vehicle Model (QVM) for road elevation measurement to enable each connected vehicle to provide valid data for a potential crowd sensing approach where every vehicle contributes data for past and consumes data for upcoming segments. The generated data is capable of providing the environment model with real-time data of upcoming road segments. The simulation results are validated on a test bench including a review of the errors.