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This thesis addresses the problem of terrain classification in unstructured outdoor environments. Terrain classification includes the detection of obstacles and passable areas as well as the analysis of ground surfaces. A 3D laser range finder is used as primary sensor for perceiving the surroundings of the robot. First of all, a grid structure is introduced for data reduction. The chosen data representation allows for multi-sensor integration, e.g., cameras for color and texture information or further laser range finders for improved data density. Subsequently, features are computed for each terrain cell within the grid. Classification is performedrnwith a Markov random field for context-sensitivity and to compensate for sensor noise and varying data density within the grid. A Gibbs sampler is used for optimization and is parallelized on the CPU and GPU in order to achieve real-time performance. Dynamic obstacles are detected and tracked using different state-of-the-art approaches. The resulting information - where other traffic participants move and are going to move to - is used to perform inference in regions where the terrain surface is partially or completely invisible for the sensors. Algorithms are tested and validated on different autonomous robot platforms and the evaluation is carried out with human-annotated ground truth maps of millions of measurements. The terrain classification approach of this thesis proved reliable in all real-time scenarios and domains and yielded new insights. Furthermore, if combined with a path planning algorithm, it enables full autonomy for all kinds of wheeled outdoor robots in natural outdoor environments.
The publication of freely available and machine-readable information has increased significantly in the last years. Especially the Linked Data initiative has been receiving a lot of attention. Linked Data is based on the Resource Description Framework (RDF) and anybody can simply publish their data in RDF and link it to other datasets. The structure is similar to the World Wide Web where individual HTML documents are connected with links. Linked Data entities are identified by URIs which are dereferenceable to retrieve information describing the entity. Additionally, so called SPARQL endpoints can be used to access the data with an algebraic query language (SPARQL) similar to SQL. By integrating multiple SPARQL endpoints it is possible to create a federation of distributed RDF data sources which acts like one big data store.
In contrast to the federation of classical relational database systems there are some differences for federated RDF data. RDF stores are accessed either via SPARQL endpoints or by resolving URIs. There is no coordination between RDF data sources and machine-readable meta data about a source- data is commonly limited or not available at all. Moreover, there is no common directory which can be used to discover RDF data sources or ask for sources which offer specific data. The federation of distributed and linked RDF data sources has to deal with various challenges. In order to distribute queries automatically, suitable data sources have to be selected based on query details and information that is available about the data sources. Furthermore, the minimization of query execution time requires optimization techniques that take into account the execution cost for query operators and the network communication overhead for contacting individual data sources. In this thesis, solutions for these problems are discussed. Moreover, SPLENDID is presented, a new federation infrastructure for distributed RDF data sources which uses optimization techniques based on statistical information.
Traditional Driver Assistance Systems (DAS) like for example Lane Departure Warning Systems or the well-known Electronic Stability Program have in common that their system and software architecture is static. This means that neither the number and topology of Electronic Control Units (ECUs) nor the presence and functionality of software modules changes after the vehicles leave the factory.
However, some future DAS do face changes at runtime. This is true for example for truck and trailer DAS as their hardware components and software entities are spread over both parts of the combination. These new requirements cannot be faced by state-of-the-art approaches of automotive software systems. Instead, a different technique of designing such Distributed Driver Assistance Systems (DDAS) needs to be developed. The main contribution of this thesis is the development of a novel software and system architecture for dynamically changing DAS using the example of driving assistance for truck and trailer. This architecture has to be able to autonomously detect and handle changes within the topology. In order to do so, the system decides which degree of assistance and which types of HMI can be offered every time a trailer is connected or disconnected. Therefore an analysis of the available software and hardware components as well as a determination of possible assistance functionality and a re-configuration of the system take place. Such adaptation can be granted by the principles of Service-oriented Architecture (SOA). In this architectural style all functionality is encapsulated in self-contained units, so-called Services. These Services offer the functionality through well-defined interfaces whose behavior is described in contracts. Using these Services, large-scale applications can be built and adapted at runtime. This thesis describes the research conducted in achieving the goals described by introducing Service-oriented Architectures into the automotive domain. SOA deals with the high degree of distribution, the demand for re-usability and the heterogeneity of the needed components.
It also applies automatic re-configuration in the event of a system change. Instead of adapting one of the frameworks available to this scenario, the main principles of Service-orientation are picked up and tailored. This leads to the development of the Service-oriented Driver Assistance (SODA) framework, which implements the benefits of Service-orientation while ensuring compatibility and compliance to automotive requirements, best-practices and standards. Within this thesis several state-of-the-art Service-oriented frameworks are analyzed and compared. Furthermore, the SODA framework as well as all its different aspects regarding the automotive software domain are described in detail. These aspects include a well-defined reference model that introduces and relates terms and concepts and defines an architectural blueprint. Furthermore, some of the modules of this blueprint such as the re-configuration module and the Communication Model are presented in full detail. In order to prove the compliance of the framework regarding state-of-the-art automotive software systems, a development process respecting today's best practices in automotive design procedures as well as the integration of SODA into the AUTOSAR standard are discussed. Finally, the SODA framework is used to build a full-scale demonstrator in order to evaluate its performance and efficiency.