Applying Quarter-Vehicle Model Simulation for Road Elevation Measurements Utilizing the Vehicle Level Sensor

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.