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|Title:||Application of machine learning techniques in vehicle collision detection||Authors:||Low, Xian Hao||Keywords:||Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Low, X. H. (2022). Application of machine learning techniques in vehicle collision detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158253||Abstract:||Vehicle 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.||URI:||https://hdl.handle.net/10356/158253||Schools:||School of Electrical and Electronic Engineering||Research Centres:||Continental-NTU Corporate Lab in RTP||Fulltext Permission:||embargo_restricted_20240516||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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|Low Xian Hao - FYP report.pdf|
|1.58 MB||Adobe PDF||Under embargo until May 16, 2024|
Updated on Dec 9, 2023
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