Reinhard German

Florian Bock, Daniel Homm, Sebastian Siegl et al.

Within the growing domain of software engineering in the automotive sector, the number of used tools, processes, methods and languages has increased distinctly in the past years. To be able to choose proper methods for particular development use cases, factors like the intended use, key-features and possible limitations have to be evaluated. This requires a taxonomy that aids the decision making. An analysis of the main existing taxonomies revealed two major deficiencies: the lack of the automotive focus and the limitation to particular engineering method types. To face this, a graphical taxonomy is proposed based on two well-established engineering approaches and enriched with additional classification information. It provides a self-evident and -explanatory overview and comparison technique for engineering methods in the automotive domain. The taxonomy is applied to common automotive engineering methods. The resulting diagram classifies each method and enables the reader to select appropriate solutions for given project requirements.

Christian Hartmann, Anna Yupatova, Marc Reichenbach et al.

Image processing applications are common in every field of our daily life. However, most of them are very complex and contain several tasks with different complexities which result in varying requirements for computing architectures. Nevertheless, a general processing scheme in every image processing application has a similar structure, called image processing pipeline: (1) capturing an image, (2) pre-processing using local operators, (3) processing with global operators and (4) post-processing using complex operations. Therefore, application-specialized hardware solutions based on heterogeneous architectures are used for image processing. Unfortunately the development of applications for heterogeneous hardware architectures is challenging due to the distribution of computational tasks among processors and programmable logic units. Nowadays, image processing systems are started from scratch which is time-consuming, error-prone and inflexible. A new methodology for modeling and implementing is needed in order to reduce the development time of heterogenous image processing systems. This paper introduces a new holistic top down approach for image processing systems. Two challenges have to be investigated. First, designers ought to be able to model their complete image processing pipeline on an abstract layer using UML. Second, we want to close the gap between the abstract system and the system architecture.