Linear estimator for road departure warning systems

Most single vehicle road departure accidents in America occur due to either loss of control or road/lane departure caused by speeding or driver inattentiveness. Many active safety systems currently in use or under development are aimed at preventing accidents either by countering vehicle instability or by trying to prevent road departure. In either case these active safety systems need clean and reliable real-time vehicle dynamics variables to accurately assess the threat levels. Since it is not always feasible to measure the required information, estimation techniques are commonly used to fill in the gap. In this paper, we developed a Kalman filter to estimate two vehicle-handling variables that are costly to measure lateral velocity and relative heading angle. It is shown that it is critical to first obtain an accurate estimation of road super-elevation (bank angle) before those two states can be accurately estimated. By properly assigning the Kalman filter observer gains, we achieved robust estimation performance across a wide array of uncertain conditions. The work reported here would be used to support the data analysis for the road departure crash warning (RDCW) field operational test, to be carried out at the University of Michigan Transportation Research Institute (UMTRI).