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Many positioning systems employed to track motor vehicles use location techniques that were not designed solely for this purpose (GPS, terrestrial radio signals). As such they fail to utilise the restriction of motor vehicles to the road network and thus a valuable source of position information is lost. 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 have addressed 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 one-dimensional 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.

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