<p>To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. </p><p>In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. </p><p>In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.</p>

To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.