Modeling dangerous driving events based on in-vehicle data using random forest and recurrent neural network

Modern vehicles produce big data with a wide variety of formats due to missing open standards. Thus, abstractions of such data in the form of descriptive labels are desired to facilitate the development of applications in the automotive domain. We propose an approach to reduce vehicle sensor data into semantic outcomes of dangerous driving events based on aggressive maneuvers. The supervised time-series classification is implemented with Random Forest and Recurrent Neural Network separately. Our approach works with signals of a real vehicle obtained through a back-end solution, with the challenge of low and variable sampling rates. We introduce the idea of having a dangerous driving classifier as the first discriminant of relevant instances for further enrichment (e.g., type of maneuver). Additionally, we suggest a method to increment the number of driving samples for training machine learning models by weighting the window instances based on the portion of the labeled event they include. We show that a dangerous driving classifier can be used as a first discriminant to enable data integration and that transitions in driving events are relevant to consider when the dataset is limited, and sensor data has a low and unreliable frequency.