Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158253
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
Fulltext Permission: embargo_restricted_20240516
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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  Until 2024-05-16
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