Reading and understanding large amounts of sensor data from vehicle test drives becomes more and more important. In order to test vehicle components or analyze exhaust emissions in real test drives, the sensor data obtained from these test drives have to be comparable. Otherwise components or exhaust emissions are tested and analyzed under false conditions. The sensor data obtained during test drives are highly multidimensional which makes it even more complicated to identify recurring patterns. We present a process model to compare different test drives according to their sensor data and so give an answer to the question whether or not test drives in different cities, locations and environments are representative to real driving scenarios. The algorithms we use focus on segmentation of the individual multivariate test drive data and on clustering of the segments according to different methods. We present several segmentation and cluster methods and compare which of them is best suited for comparing test drives. The segmentation method we identified as best suited is based on principal component analysis. As cluster methods we examine hierarchical, partitioning and density-based clustering in detail.

Testing is an important and wide spread practice in the development of automotive components. For the design of test methods two types of input data are often considered: (1) load data gathered from real life vehicle fleets, and (2) information of the driving routes based on road features. The development of new technologies is though complicated not only by the need to join those two data sources, but also by the too limited knowledge of the parameters and their useful combinations. As a result, information about representative driving profiles is needed. To address these problems we present a visual analytics approach for analyzing multivariate trajectories as a combination of vehicle's location and road elevation data. Our system combines trajectory clustering, intervalbased user-driven trip segmentation, and frequent sequences analysis, supported by contingency table and interval-based Parallel Coordinates visualization and enables the expert user to find representative driving profiles for the definition of very compact test courses.