Linear estimator for road departure warning systems

• Published in: 2004 American Control Conference Proceedings; Volume 3 of 6 Vol. 3; pp. 2104 - 2109 vol.3
• Main Authors: Mudaliar, N., LeBlanc, D., Huei Peng
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Piscataway NJ IEEE 01.01.2004; Evanston IL American Automatic Control Council
• Subjects: Alarm systems; Applied sciences; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Modelling and identification; Real time systems; Road accidents; Road safety; Road vehicles; State estimation; Vehicle driving; Vehicle dynamics; Vehicle safety; Velocity measurement; Data analysis; Motor car; Gain estimation; Road vehicle; Pavement; Kalman filter; Real time; Vehicle dynamics; Robust control; Tilt angle; Safety systems; Uncertain system; Active system; Observer; Accident prevention; Instability; System identification
• ISBN: 9780780383357; 0780383354
• ISSN: 0743-1619; 2378-5861
• DOI: 10.23919/ACC.2004.1383771

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).