O'Keefe, KyleAmirloo Abolfathi, Elmira2015-03-112015-06-232015-03-112015http://hdl.handle.net/11023/2112Vehicle positioning is an important component of intelligent transportation systems. Due to the relative low cost and low complexity of Global Navigation Satellite Systems (GNSS), the automotive industry has adopted this technology to provide vehicle position. GNSSs solution are reliable in open sky environments. However, position solutions are needed in the areas where there is poor GNSS accuracy or availability and other sensors may be required This thesis presents the integration of vision-derived bearing measurements with between vehicle GNSS relative navigation. Two methods: target detection and machine learning based vehicle recognition, have been developed to obtain bearing measurements from the images. The resulting bearings are then integrated with a GNSS solution resulting in improved position accuracy and availability. The usefulness of the GNSS solution to improve the vehicle recognition algorithm is also investigated.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Engineeringmaster thesis10.11575/PRISM/26582