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[{"key": "dc.contributor.author", "value": "Khan, Ausaf", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2023-12-04T07:17:12Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2023-12-04T07:17:12Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2023", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/92172", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4ss\u00e4 opinn\u00e4ytteess\u00e4 tutkitaan tapoja hy\u00f6dynt\u00e4\u00e4 teko\u00e4ly\u00e4 (AI) kuljettajan ajok\u00e4ytt\u00e4ytymisen analysointiin. Tavoitteena on l\u00f6yt\u00e4\u00e4 korrelaatio niiden eri tekij\u00f6iden v\u00e4lilt\u00e4 jotka vaikuttavat kuljettajan tekemiin p\u00e4\u00e4t\u00f6ksiin hy\u00f6dynt\u00e4en kuljettajan ajoymp\u00e4rist\u00f6st\u00e4 ja tiest\u00f6st\u00e4 saatavilla olevaa ja ker\u00e4tty\u00e4 tietoa. T\u00e4m\u00e4n pohjalta voidaan jatkossa kehitt\u00e4\u00e4 j\u00e4rjestelm\u00e4 joka auttaa kuljettajia kiinnitt\u00e4m\u00e4\u00e4n huomiota ajotapaan ja parantamaan ajamisen tehokkuutta ja v\u00e4hent\u00e4m\u00e4\u00e4n ajamisesta aiheutuu ymp\u00e4rist\u00f6n kuormitusta.\nAplicom Oy on toimittanut ty\u00f6t\u00e4 varten T10G-telematiikkalaitteen, josta l\u00f6ytyy mittalaitteet ajoneuvon nopeuden, sijainnin ja ajan seuraamiseksi. Lis\u00e4ksi laitteesta l\u00f6ytyy CAN-liit\u00e4nt\u00e4, jonka avulla p\u00e4\u00e4st\u00e4\u00e4n lukemaan ajoneuvon omia antureita. N\u00e4iden tietojan avulla kuljettajan ajotapaa voidaan analysoida. Opinn\u00e4ytteen l\u00e4ht\u00f6tiedoiksi ajotietoa on ker\u00e4tty useilla ajokerroilla Suomessa kaupunkiliikenteest\u00e4 ja moottoritieajosta. Analyysissa ker\u00e4tyt tiedot on yhdistetty Trafin julkisen Digiroad-aineiston kanssa josta on saatu tiest\u00f6n yksityiskohtaista nopeusrajoitus- ja geometriatietoa. Muodostetusta tietomallista voidaan muodostaa tarkka kuva kuljettajan ajok\u00e4yt\u00f6ksest\u00e4 kyseisell\u00e4 tieosuudella.\nOpinn\u00e4yte havainnollistaa osuvalla tavalla kuinka koneoppimista voidaan hy\u00f6dynt\u00e4\u00e4 liikenteess\u00e4 kuljettajan avustamiseksi tehden ajamisesta turvallisempaa, tehokkaampaa ja ymp\u00e4rist\u00f6yst\u00e4v\u00e4llisemp\u00e4\u00e4. Tulevaisuuden liikenteess\u00e4 tullaan hy\u00f6dynt\u00e4m\u00e4\u00e4n entist\u00e4 enemm\u00e4n tietokoneen ja kuljettajan yhteisty\u00f6t\u00e4 jotta vastuullisuuden, turvallisuuden ja kest\u00e4v\u00e4n kehityksen tavoitteet voidaan saavuttaa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This thesis examines the ways in which artificial intelligence (AI) can be used to study the impact of driving patterns, aiming to find a correlation between the variables influencing the driver's decision-making process by using data that can be gathered in various driving environments and terrains. This analysis will be helpful in developing a system that helps drivers modify their driving habits for increased vehicle efficiency and reduced damage to the environment. A T10G device from Aplicom Oy, containing important interfaces to the vehicle sensors via a CAN bus interface and on-device sensors that measure vehicle speed, latitude longitude and timestamp data, is used to analyze driving behavior. Data has been collected on multiple journeys both in-city and on highway in Finland and mapped onto the Finnish Transport Infrastructure Authority\u2019s publicly available API which contains detailed mapping of Finnish road and street networks as well as winter and summer speed limits with Geo coordinates, thus providing an accurate picture of driving behavior along the aforementioned path. This study expresses how incorporating machine learning is a funda-mental shift in driving that will make it safer, more efficient, and environmentally friendly. The driving experience of the future will see more involvement from human-machine inter-action based on sustainability, safety, and accountability.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2023-12-04T07:17:12Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2023-12-04T07:17:12Z (GMT). No. of bitstreams: 0\n Previous issue date: 2023", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "77", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "driving behavior", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "driving efficiency", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Using AI to study impact of driving patterns", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202312048170", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", 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"telematiikka", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "liikennek\u00e4ytt\u00e4ytyminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "teko\u00e4ly", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "telematics", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "traffic behaviour", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "artificial intelligence", "language": null, "element": "subject", 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