Learning an Embedding for Vehicle Telematics
Name
leonard-mattdl-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
4.69 MB
Format
Adobe PDF
Checksum (MD5)
a60cc8be203cec7acba95e5ae9b22700
Author(s)
Leonard, Matthew
Advisor(s)
Madden, Samuel
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Vehicular telematics involves the collection and processing of data about driving behavior; however, mining and modeling this data is difficult due to its large volume. We hypothesize that the data will follow regular patterns of events that occur during drives, and that we can learn these patterns. With this intuition, we design a neural network that will effectively embed sections of accelerometer data into a lower-dimensional space, with a low loss of information and accuracy of the embedding relative to the dimensionality reduction, as well as several other desirable geometric properties for indexing and analysis of the data. We further develop an accurate summary of the distribution of each drive in this lower-dimensional space, which would serve as a proxy for the occurrence of events within these drives. From this system, we develop a method of comparison between different drives that highlights whether or not particular events occurred in each drive. This could be used to develop a more robust and nuanced risk model, and determine which events in a drive are associated with risk, to provide feedback to end users on their driving.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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