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Brake system development and testing is spread over vehicle manufacturers, system and component suppliers. Test equipment from different sources, even resulting from different technology generations, different data analysis and report tools - comprising different and sometimes undocumented algorithms - lead to a difficult exchange and analysis of test results and, at the same time, contributes to unwanted test variability. Other studies regarding the test variability brought up that only a unified and unambiguous data format will allow a meaningful and comparative evaluation of these data and only standardization will reveal the actual reasons of test variability. The text at hand illustrates that a substantial part of test variability is caused by a misinterpretation of data and/or by the application of different algorithms.

Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.

Automotive electric and electronic (E/E) systems are key drivers for innovation in today's vehicles. While new functions are delivering eco-friendliness (hybrid and pure electric vehicles, etc.), assistance/comfort (drive-by-wire, park-assist, etc.) and active safety (electronic stability control, lane-change-assist, brake-assist, etc.) their inherent complexity is challenging manufacturers and suppliers. At the same time, functional safety of the product is a key issue: During the whole car's product life cycle, there are many potential risks for physical injuries, or even worse, fatalities. Therefore, these potential sources of harm should strictly be avoided. In this work, we focus on a powerful method for verification and validation activities during early phases of the development, namely simulation. Simulation is one of the main methods for verification stated by the functional safety standard ISO 26262.

Tire mark analysis is an important factor in accident reconstruction. A precise determination of pre- and postcrash speeds as well as longitudinal and lateral accelerations from tire marks contributes significantly to a reliable accident reconstruction. Continuous advancements in tire and vehicle technology – in particular with respect to modern control systems such as anti-lock braking systems (ABS) – raises the question what role tire marks play in accident reconstruction today. Moreover, this accompanies the question to what extent potential interventions by vehicle control systems such as the electronic stability program (ESP®) resp. the electronic stability control (ESC) can be identified in a tire mark. The widespread use of these systems today makes them increasingly important in accident reconstruction.