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.

Die simulative Absicherung von Produkten in den frühen Phasen der Produktentwicklung wird immer wichtiger, um den Anforderungen nach steigender Effizienz gerecht zu werden. Da das Angebot an erfahrenen Berechnungsingenieuren mit langjähriger Berufserfahrung begrenzt ist gilt es weniger erfahrene Simulationsanwender bei der Durchführung von aussagekräftigen Finite-Elemente-Simulationen zu unterstützen. Die Autoren stellen im Rahmen des Beitrags das Konzept des Analyseteils FEdelM eines FEA-Assistenzsystems vor, welches strukturmechanische Finite-Elemente (FE) Simulationen auf Plausibilität überprüft und auftretende Fehler möglichst automatisiert zu erkennt und behebt. Hierbei werden die einzelnen Module und deren Verknüpfungen untereinander und zu anderen Anwendungen vorgestellt.

The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

Durch die Entwicklung und Einführung moderner Fahrerassistenzsysteme soll der Komfort und die Sicherheit des Autofahrens erhöht werden. Das Fahrerassistenzsystem Speed Control verbindet die Ergebnisse der Forschungsarbeiten der letzten Jahre aus dem Bereich des Straßenentwurfs und der Fahrzeugtechnik. Dieses System warnt den Kraftfahrer vor sicherheitskritischen Stellen in der Linienführung von Außerortsstraßen. Es empfiehlt dem Fahrer eine sicher und komfortabel fahrbare Geschwindigkeit für den vorausliegenden Streckenabschnitt. Dafür wurden in Abhängigkeit des Fahrertyps Modelle zur Prognose und Beschreibung des Geschwindigkeits- und Beschleunigungsverhaltens entwickelt. Die Umgebungsbedingungen (Tag, nass) werden dabei mit beachtet. / By developing and implementing modern driver assistance systems the comfort and safety of driving shall be improved. The driver assistance system Speed Control combines the last year’s research work results in the fields of road design and automotive engineering. This system alerts the driver to safety critic spots in the alignment of roads. It recommends a safe and comfortable driving speed for the road segment ahead. Therefore driver type depending models to predict and describe the speed and acceleration behaviour were developed. Withal environmental conditions (day, wet) are regarded.

This thesis work aims at performing a survey of those technologies generally called DriverAssistance Systems (DAS). This thesis work focuses on gathering information in terms ofaccident statistics, sensors and functions and analyzing this information and shall thruaccessible information match functions with accidents, functions with sensors etc.This analysis, based on accidents in United States and Sweden during the period 1998 – 2002and two truck accident studies, shows that of all accidents with fatalities or sever injuriesinvolving a heavy truck almost half are the result of a frontal impact. About one fourth of theaccidents are caused by side impact, whereas single vehicle and rear impact collisions causesaround 14 % each. Of these, about one fourth is collision with unprotected (motorcycles,mopeds, bicycles, and pedestrians) whereas around 60 % are collision with other vehicles.More than 90 % of all accidents are partly the result of driver error and about 75 % aredirectly the result of driver error. Hence there exist a great opportunity to reduce the numberof accidents by introducing DAS.In this work, an analysis of DAS shows that six of the systems discussed today have thepotential to prevent 40 – 50 % of these accidents, whereas 20 – 40 % are estimated to actuallyhaving the chance to be prevented.One of these DAS, automatic emergency brake (AEB), has been analyzed in more detail.Decision models for an emergency brake capable to mitigate rear-end accidents has beendesigned and evaluated. The results show that this model has high capabilities to mitigatecollisions.

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