Learning an Embedding for Vehicle Telematics
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....
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156792 |
| _version_ | 1826191807359221760 |
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| author | Leonard, Matthew |
| author2 | Madden, Samuel |
| author_facet | Madden, Samuel Leonard, Matthew |
| author_sort | Leonard, Matthew |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:01:36Z |
| format | Thesis |
| id | mit-1721.1/156792 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:01:36Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1567922024-09-17T03:59:27Z Learning an Embedding for Vehicle Telematics Leonard, Matthew Madden, Samuel Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. M.Eng. 2024-09-16T13:49:26Z 2024-09-16T13:49:26Z 2024-05 2024-07-11T14:36:40.828Z Thesis https://hdl.handle.net/1721.1/156792 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Leonard, Matthew Learning an Embedding for Vehicle Telematics |
| title | Learning an Embedding for Vehicle Telematics |
| title_full | Learning an Embedding for Vehicle Telematics |
| title_fullStr | Learning an Embedding for Vehicle Telematics |
| title_full_unstemmed | Learning an Embedding for Vehicle Telematics |
| title_short | Learning an Embedding for Vehicle Telematics |
| title_sort | learning an embedding for vehicle telematics |
| url | https://hdl.handle.net/1721.1/156792 |
| work_keys_str_mv | AT leonardmatthew learninganembeddingforvehicletelematics |