Publication
Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods
| Summary: | A private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants’ routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle’s users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior. |
|---|---|
| Main Authors: | Barbosa, Paulo |
| Other Authors: | Ferreira, Flora; Fernandes, Carlos; Erlhagen, Wolfram; Guimarães, Pedro; Wojtak, Weronika; Monteiro, Sérgio; Bicho, Estela |
| Subject: | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| Year: | 2022 |
| Country: | Portugal |
| Document type: | conference paper |
| Access type: | open access |
| Associated institution: | Universidade do Minho |
| Language: | English |
| Origin: | RepositóriUM - Universidade do Minho |
| _version_ | 1866270648382259200 |
|---|---|
| author | Barbosa, Paulo |
| author2 | Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimarães, Pedro Wojtak, Weronika Monteiro, Sérgio Bicho, Estela |
| author2_role | author author author author author author author |
| author_facet | Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimarães, Pedro Wojtak, Weronika Monteiro, Sérgio Bicho, Estela |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Barbosa, Paulo\"},{\"Person.name\":\"Ferreira, Flora\"},{\"Person.name\":\"Fernandes, Carlos\"},{\"Person.name\":\"Erlhagen, Wolfram\"},{\"Person.name\":\"Guimarães, Pedro\"},{\"Person.name\":\"Wojtak, Weronika\"},{\"Person.name\":\"Monteiro, Sérgio\"},{\"Person.name\":\"Bicho, Estela\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimarães, Pedro Wojtak, Weronika Monteiro, Sérgio Bicho, Estela |
| datacite.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-11T09:42:10Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-11T09:42:10Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| datacite.titles.title.fl_str_mv | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimarães, Pedro Wojtak, Weronika Monteiro, Sérgio Bicho, Estela |
| dc.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-11T09:42:10Z |
| dc.date.embargoed.fl_str_mv | 2024-01-11T09:42:10Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/88047 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| dc.title.fl_str_mv | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | A private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants’ routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle’s users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/e7723c82-be88-401d-b5f5-7112c5898385/download |
| id | rum_8da6152749febb8a7d0ceea96391b4d4 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/88047 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/88047 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimarães, Pedro Wojtak, Weronika Monteiro, Sérgio Bicho, Estela |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Springer |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engSpringerporA private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants’ routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle’s users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior.application/pdfporEndowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methodsBarbosa, PauloFerreira, FloraFernandes, CarlosErlhagen, WolframGuimarães, PedroWojtak, WeronikaMonteiro, SérgioBicho, EstelaHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISBNIsPartOf9783031217524ISSNIsPartOf0302-9743DOIIsPartOf10.1007/978-3-031-21753-1_162024-01-11T09:42:10Z20222022-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/88047http://purl.org/coar/access_right/c_abf2open accessHuman-centered computingMachine learningDriver routinesLearning driver habitsHuman mobility patternsTime and space predictionDeep learningIntelligent vehicles3881788 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/e7723c82-be88-401d-b5f5-7112c5898385/download |
| spellingShingle | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods Barbosa, Paulo Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| status | SINGLETON |
| subject.fl_str_mv | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| title | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| title_full | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| title_fullStr | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| title_full_unstemmed | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| title_short | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| title_sort | Endowing intelligent vehicles with the ability to learn user’s habits and preferences with machine learning methods |
| topic | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| topic_facet | Human-centered computing Machine learning Driver routines Learning driver habits Human mobility patterns Time and space prediction Deep learning Intelligent vehicles |
| url | https://hdl.handle.net/1822/88047 |
| visible | 1 |