Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158253
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dc.contributor.authorLow, Xian Haoen_US
dc.date.accessioned2022-06-02T02:53:32Z-
dc.date.available2022-06-02T02:53:32Z-
dc.date.issued2022-
dc.identifier.citationLow, X. H. (2022). Application of machine learning techniques in vehicle collision detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158253en_US
dc.identifier.urihttps://hdl.handle.net/10356/158253-
dc.description.abstractVehicle accidents are still happening daily even with the existing technological support provided. This can be due to limitations of the technology and/or human error. Using a newer technology, the vehicle-to-everything communication, the aim is to use machine learning techniques to make predictions on GPS data, in order to provide an early collision warning system. With such a system in place, drivers would be alerted if a collision might happen several seconds prior and be mentally prepared for the potential threat. The algorithms explored in this study is the multi-layered perceptron classifier, random forest and Tabnet.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleApplication of machine learning techniques in vehicle collision detectionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorGuan Yong Liangen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Information Engineering and Media)en_US
dc.contributor.researchContinental-NTU Corporate Lab in RTPen_US
dc.contributor.supervisoremailEYLGuan@ntu.edu.sgen_US
item.grantfulltextembargo_restricted_20240516-
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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