Robust Route Prediction in Raster Maps

By adapting gear changes and cruise control of a Heavy Duty Vehicle (HDV) to road inclination, fuel and time savings can be achieved. In this thesis is presented a novel method of predicting the upcoming road topography, by constructing a geographical and topographical self-learning map from which a route prediction is made. The system is designed to simultaneously produce the desired road grade output in real time and update the map each time an area is driven through, making the map more accurate as more data is collected. Special considerations are given to the memory and processing constraints of embedded automotive control hardware.