Vehicle parameter estimation using a model-based estimator

In the last few years, many closed-loop control systems have been introduced in the automotive field to increase the level of safety and driving automation. For the integration of such systems, it is critical to estimate motion states and parameters of the vehicle that are not exactly known or that change over time. This paper presents a model-based observer to assess online key motion and mass properties. It uses common onboard sensors, i.e. a gyroscope and an accelerometer, and it aims to work during normal vehicle manoeuvres, such as turning motion and passing. First, basic lateral dynamics of the vehicle is discussed. Then, a parameter estimation framework is presented based on an Extended Kalman filter. Results are included to demonstrate the effectiveness of the estimation approach and its potential benefit towards the implementation of adaptive driving assistance systems or to automatically adjust the parameters of onboard controllers.