Estimation and prediction of vehicle dynamics states based on fusion of OpenStreetMap and vehicle dynamics models

This paper presents a novel approach for estimation and prediction of vehicle dynamics states by incorporating digital road map and vehicle dynamics models. Precise information about vehicle dynamics states is essential for the safety and stability of vehicle. In particular, the tire-road contact forces and vehicle side slip angle are the most important parameters for evaluating the safety of vehicle. Nevertheless, these dynamics states are immeasurable with low cost sensors. Therefore, different observers, or the so-called virtual sensors are developed to estimate vehicle dynamics states. However, the existing observers are only capable in estimating vehicle dynamics states at a current instant but not to predict the potential dangers in a future instant. In order to make time for correcting drive behaviors, especially when driving at high speed, it seems very appealing for us to predict an impending dangerous event and react before the danger occurs. In this paper, the estimation of vehicle dynamics states is based on the fusion of information from inertial sensors, GPS and OpenStreetMap. The geometry of the upcoming path ahead of vehicle is provided by the digital map and is employed to predict the future dynamics states.