Comparing multidimensional sensor data from vehicle fleets with methods of sequential data mining

Abstract

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.