Improved Vehicle Longitudinal Velocity Estimation Using Accelerometer Based Intelligent Tire

Abstract

An intelligent tire–based algorithm was developed to reinforce the vehicle longitudinal velocity estimation, from the vehicle inertial measurement unit (IMU). A tire was instrumented using a triaxis accelerometer (intelligent tire) in an instrumented vehicle with an IMU, and a global positioning system (GPS) based speed sensor (VBOX) as the ground truth for vehicle velocity. A testing matrix was developed, including two tire inflation pressures, two normal loads, and variable speed between 4 m/s to 14 m/s. A signal processing algorithm was developed to analyze the data from the accelerometer. Variational mode decomposition and Hilbert spectrum analysis were used for extracting features from each tire revolution. Later, a machine learning algorithm was trained to estimate the velocity using the acceleration data from the intelligent tire. Because the sampling rates of the IMU data and the intelligent tire data were different, sensor fusion was implemented. This calculated velocity was then used to correct the IMU-based estimated velocity. This new velocity can be used to enhance the performance of all advanced chassis control systems, such as ABS and ESP.