Discovering Patterns in Driving Data

Abstract

Modern vehicles are equipped with numerous sensors for providing feedback to the control unit. These measurements hold a substantial amount of information about the driver’s action, environmental and traffic conditions. In this thesis, we investigate using various machine leaning techniques to analyze driving data for discovering repetitive patterns when facing similar traffic situations. To this end, we first use unsupervised learning and data mining techniques to find driving patterns and to develop a labeling scheme. This last point consist of finding patterns in individual signals which are then combined to find patterns describing more complex behaviors. The discovered patterns and labels are used in the second part of the thesis to develop a classifier for recognizing the current driving situation. The classifier is designed such that it can be implemented in an Electric Control Unit of a production vehicle. After the analysis, we were able to discover intelligible driving scenarios and we focused on some of them to label our data. We used this labeling to train and compare four different neural network architectures commonly used in time series classification. The models are trained by simulating an online situation where data comes in a form of data stream. The results show that online classification is feasible. Implementing the classifier in the vehicle software could be beneficial for aiding the control unit in deciding gear shifts, energy recuperation and propulsion system. This may lead to a more efficient vehicle and a better driving experience.