Road Estimation Using GPS Traces and Real Time Kinematic Data

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods

Road Estimation Using GPS Traces and Real Time Kinematic Data

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods

Road Estimation Using GPS Traces and Real Time Kinematic Data

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods