Discovering Patterns in Driving Data
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2019
Författare
Baca Mendoza, Karen
Söderkvist Vermelin, Wilhelm
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
pattern discovery, machine learning, clustering, data mining, classification, neural networks, time series data, traffic scenarios, vehicle control