Discovering Groups Of Signals In In-Vehicle Network Traces For Redundancy Detection And Functional Grouping

Modern vehicles exchange signals across multiple ECUs in order to run various functionalities. With increasing functional complexity the amount of distinct signals grew too large to be analyzed manually. During development of a car only subsets of such signals are relevant per analysis and functional group. Moreover, historical growth led to redundancies in signal specifications which need to be discovered. Both tasks can be solved through the discovery of groups. While the analysis of in-vehicle signals is increasingly studied, the grouping of relevant signals as a basis for those tasks was examined less. We therefore present and extensively evaluate a processing and clustering approach for semi-automated grouping of in-vehicle signals based on traces recorded from fleets of cars.