A vehicle classification algorithm based on telematics data
Author(s)Nguyen, Linh Vuong
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Tomas Palacios, Hari Balakrishnan and Bill Bradley.
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In the thesis, I develop an algorithm to identify the vehicle model from telematics data. By extracting the features from the accelerometer and GPS data, we obtain the classification features, which then goes through a multiclass random forest classifier. We apply this results into problems of driver and vehicle identification. The result shows that, while the algorithm could identify the vehicle models to some extent, the dominating signal comes from driving style, and an approach running purely unsupervised learning is harder to achieve good classification results compared to supervised methods.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 45-46).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.