Driver behavior profiling: An investigation with different smartphone sensors and machine learning
Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
PLOS ONE
2021
|
| Online Access: | https://hdl.handle.net/1721.1/130308 |
| _version_ | 1824458196751745024 |
|---|---|
| author | Ferreira Junior, Jair Carvalho, Eduardo Ferreira, Bruno V. de Souza, Cleidson Suhara, Yoshihiko Pentland, Alex Pessin, Gustavo |
| author2 | MIT Connection Science (Research institute) |
| author_facet | MIT Connection Science (Research institute) Ferreira Junior, Jair Carvalho, Eduardo Ferreira, Bruno V. de Souza, Cleidson Suhara, Yoshihiko Pentland, Alex Pessin, Gustavo |
| author_sort | Ferreira Junior, Jair |
| collection | MIT |
| description | Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. |
| first_indexed | 2024-09-23T13:10:09Z |
| format | Article |
| id | mit-1721.1/130308 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:22:03Z |
| publishDate | 2021 |
| publisher | PLOS ONE |
| record_format | dspace |
| spelling | mit-1721.1/1303082025-02-06T18:53:04Z Driver behavior profiling: An investigation with different smartphone sensors and machine learning Ferreira Junior, Jair Carvalho, Eduardo Ferreira, Bruno V. de Souza, Cleidson Suhara, Yoshihiko Pentland, Alex Pessin, Gustavo MIT Connection Science (Research institute) Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. 2021-03-31T18:16:44Z 2021-03-31T18:16:44Z 2017-04-10 Article https://hdl.handle.net/1721.1/130308 Ferreira, J., Carvalho, E., Ferreira, B. V., de Souza, C., Suhara, Y., Pentland, A., & Pessin, G. (2017). Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS one, 12(4), e0174959. en Attribution-NonCommercial-ShareAlike 3.0 United States http://creativecommons.org/licenses/by-nc-sa/3.0/us/ application/pdf PLOS ONE |
| spellingShingle | Ferreira Junior, Jair Carvalho, Eduardo Ferreira, Bruno V. de Souza, Cleidson Suhara, Yoshihiko Pentland, Alex Pessin, Gustavo Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title | Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title_full | Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title_fullStr | Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title_full_unstemmed | Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title_short | Driver behavior profiling: An investigation with different smartphone sensors and machine learning |
| title_sort | driver behavior profiling an investigation with different smartphone sensors and machine learning |
| url | https://hdl.handle.net/1721.1/130308 |
| work_keys_str_mv | AT ferreirajuniorjair driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT carvalhoeduardo driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT ferreirabrunov driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT desouzacleidson driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT suharayoshihiko driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT pentlandalex driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning AT pessingustavo driverbehaviorprofilinganinvestigationwithdifferentsmartphonesensorsandmachinelearning |