DGPS/INS integrated positioning for control of automated vehicle

Abstract

In recent years, the Global Positioning System (GPS) has solidified its presence as a dependable means of navigation by providing absolute positioning in various applications. While GPS alone can provide position information, it has several weaknesses, such as low data output rate and vulnerability to external disturbances. We explore the feasibility of an integrated positioning system using a differential GPS (DGPS) and an inertial navigation system (INS) for the control of an automated vehicle. An extended Kalman filter which combines the measurements from the DGPS, INS, and vehicle sensors to produce estimates of various vehicle states is derived. A methodology which, using map data, converts position measurements to vehicle lateral offset and desired speed, as applicable for the control of an automated vehicle, is presented. An analysis of the overall closed-loop vehicle control system is discussed. Finally, the performance of the proposed control scheme is examined through field tests conducted on two different vehicle platforms, an automated golfcart and a drive-by-wire Honda Accord sedan.