Body Motion Estimation from Non-Dedicated Sensors

Abstract

Accurate knowledge of vehicle body motion is essential for modern suspension control systems, such as continuously variable damping and air spring control. Traditionally, these systems rely on dedicated accelerometers mounted on the vehicle body. However, modern vehicles already carry inertial sensors within safety-critical subsystems such as the Supplemental Restraint System (SRS), alongside suspension deflection sensors at each wheel. This thesis investigates whether these existing onboard signals can be repurposed to estimate vehicle body accelerations, specifically heave, roll, and pitch, to a standard sufficient for suspension control, without introducing additional hardware. A Kalman filter-based sensor fusion algorithm is developed, combining SRS IMU measurements with vehicle speed from the CAN bus and suspension deflection signals. The algorithm first estimates vehicle attitude through a linear time-varying Kalman filter that propagates the gravity direction vector using gyroscope measurements and corrects it using compensated lateral and vertical accelerometer readings. The estimated attitude is then used to extract global vertical acceleration from the IMU, which is subsequently fused with suspension-derived body signals in a Kalman-based observer to produce heave, roll, and pitch acceleration estimates. The algorithm was evaluated against ground-truth signals derived from a dedicated accelerometer system currently used at Volvo Cars Corporation. The results demonstrate that heave and pitch accelerations can be reconstructed with strong accuracy across the primary ride frequency band, while roll estimation captures the dominant low-frequency dynamics with more moderate agreement. Overall, the proposed framework shows that SRS-based sensor fusion is a technically viable alternative to dedicated body accelerometers, offering a path toward reduced hardware cost and improved sensor redundancy in production vehicles.