Collective Enrichment of OpenStreetMap Spatial Data Through Vehicles Equipped with Driver Assistance Systems

Navigation systems are one of the most commonly found electronic gadgets in modern vehicles nowadays. Alongside navigation units this technology is made readily available to individuals in everyday devices such as a mobile phone. Digital maps which come preloaded on these devices accommodate within them an extensive dataset of spatial information from around the globe which aids the driver in achieving a well guided driving experience. Apart from being essential for navigation this sensor information backs up other vehicular applications in making intelligent decisions. The quality of this information delivered is in direct relation to the underlying dataset used to produce these maps. Since we live in a highly dynamic environment with constantly changing geography, an effort is necessary to keep these maps updated with the most up to date information as frequently as possible. The digital map of interest in this study is OpenStreetMap, the underlying data of which is a combination of donated as well as crowdsourced information from the last 10 years. This extensive dataset helps in building of a detailed digital map of the world using well defined cartographic techniques. The information within OpenStreetMap is currently enhanced by a large group of volunteers who willing use donated satellite imagery, uploaded GPS tracks, field surveys etc. to correct and collect necessary data for a region of interest. Though this method helps in improving and increasing the quality and quantity of the OpenStreetMap dataset, it is very time consuming and requires a great deal of human effort. Through this thesis an effort is made to automatically enrich this dataset by preprocessing crowdsourced sensor data collected from the navigation system and driver assistance systems (Traffic Sign Recognition system and a Lane Detection System) of a driving vehicle. The kind of data that is algorithmically derived includes the calculation of the curvature of the underlying road, correction of speed limit values for individual road segments being driven and the identification of change in the geometry of existing roads due to closure of old ones or addition of new ones in the Nuremberg region of Bavaria, Germany. Except for a small percentage of speed limit information on roads segments, other information is currently not available in the OpenStreetMap database for use in safety and comfort related applications. The navigation system has the ability to deliver geographical data in form of GPS coordinates at a certain frequency. This set of GPS coordinates can grouped together to form a GPS track visualizing the actual path traversed by a driving vehicle. A large number of such GPS tracks repeatedly collected from different vehicles driving in a region of interest gives all GPS points which lie on a particular road. These points, after outlier elimination methods are used as a dataset to scientifically determine the underlying curvature of the road with the aid of curve fitting techniques. Additional information received from the lane detection system helps identify curves on a road for which the curvature must be calculated. The fusion of information from these sources helps to achieve curvature results with high accuracy. Traffic sign recognition system helps detect traffic signs while driving, the fusion of this data with geographical information from the navigation system at the instance of detection helps determine road segments for which the recognized speed limit values are valid. This thesis successfully demonstrates a method to automatically enrich OpenStreetMap data by crowdsourcing raw sensor data from multiple vehicles equipped with driver assistance systems. All OpenStreetMap attributes were 100% updated into the database and the results have proven the effectiveness our system architecture. The positive results obtained in combination with minimal errors promise a better future for assisted driving