Increased accuracy of motor vehicle position estimation by utilising map data: vehicle dynamics, and other information sources

Techniques exist that make use of map information to improve the position estimate of a motor vehicle but the techniques lack a mathematical framework. The authors addresses this problem by developing a map-aided position estimation system whereby the raw position measurements are optimally translated so that they lie on the roads. The accuracy of the map-aided estimates is derived for an arbitrary positioning system with Gaussian measurement noise demonstrating significant improvements over the raw measurements. Further performance improvements are achieved through the use of a 1D Kalman filter developed to utilise the fact that all of the map-aided position estimates lie along known curves. The mathematical framework utilised by the map-aided estimator readily allows other sources of position information such as road type and road rules to be quantified and optimally incorporated into the estimation process