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undergraduate thesis
Klasifikacija navika vozača strojnim učenjem
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:933044
Galić, Matej
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Galić, M. (2024). Klasifikacija navika vozača strojnim učenjem (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:933044

Galić, Matej. "Klasifikacija navika vozača strojnim učenjem." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:933044

Galić, Matej. "Klasifikacija navika vozača strojnim učenjem." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:933044

Galić, M. (2024). 'Klasifikacija navika vozača strojnim učenjem', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:933044

Galić M. Klasifikacija navika vozača strojnim učenjem [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2025 January 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:933044

M. Galić, "Klasifikacija navika vozača strojnim učenjem", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:933044

Metadata
TitleKlasifikacija navika vozača strojnim učenjem
Title (english)Classification of driver habits with machine learning
AuthorMatej Galić
MentorVinko Lešić (mentor)
Committee memberVinko Lešić (član povjerenstva)
Committee memberFilip Vrbanc (član povjerenstva)
Committee memberMihael Jakšić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad istražuje primjenu strojnog učenja za klasifikaciju stilova vožnje vozača. Korištenjem algoritma ID3, prikupljeni podaci iz GPS-a, akcelerometra i žiroskopa analizirani su kako bi se identificirali ekološki, udobni i sportski stilovi vožnje. Rezultati pokazuju da je moguće smanjiti potrošnju goriva i emisije stakleničkih plinova optimizacijom vozačkih navika. Daljnja istraživanja mogla bi unaprijediti točnost modela i proširiti njegovu primjenu u autoindustriji i transportu.
Abstract (english)
This thesis explores the application of machine learning for classifying driving styles. Using the ID3 algorithm, data collected from GPS, accelerometer, and gyroscope were analyzed to identify eco-friendly, comfortable, and sporty driving styles. The results indicate that optimizing driving habits can reduce fuel consumption and greenhouse gas emissions. Further research could improve the model's accuracy and expand its application in the automotive and transportation industries.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:933044
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 20:37:10