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The paper focuses on an optimization methodology, which uses Design of Experiments (DoE) methods to define component parameters of parallel hybrid powertrains such as number of gears, transmission spread, gear ratios, progression factor, electric motor power, electric motor nominal speed, battery voltage and cell capacity. Target is to find the optimal configuration based on specific customer targets (e.g. fuel consumption, performance targets). In the method developed here, the hybrid drive train configuration and the combustion engine are considered as fixed components. The introduced methodology is able to reduce development time and to increase output quality of the early system definition phase. The output parameters are used as a first hint for subsequently performed detailed component development. The methodology integrates existing software tools like AVL CRUISE [5] and AVL CAMEO [1].

A main requirement for a driver assisting automated parking system is the identification and localisation of a free parking spot. A sensory system consisting of a reverse aid camera, a processing unit, vehicle track sensors and a link to the vehicles CAN-Bus is able to detect a parking spot, using vision processing algorithms. This report starts with a case differentiation regarding parking situations. In reality a combination between several attributes and conditions define the initial parking situation and the specific demand on the sensory system to detect the parking spot. The described method for camera vision based parking spot detection is characterised on specific situations, such as parking spots marked by painted lines and the kerbstone and well lighted rich-contrast situations. The method in general starts with the image processing on the incoming camera images.