Application of machine learning techniques in vehicle collision detection
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 predi...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158253 |
| _version_ | 1826114129233969152 |
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| author | Low, Xian Hao |
| author2 | Guan Yong Liang |
| author_facet | Guan Yong Liang Low, Xian Hao |
| author_sort | Low, Xian Hao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:34:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158253 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:34:27Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1582532023-07-07T18:58:21Z Application of machine learning techniques in vehicle collision detection Low, Xian Hao Guan Yong Liang School of Electrical and Electronic Engineering Continental-NTU Corporate Lab in RTP EYLGuan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence 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. Bachelor of Engineering (Information Engineering and Media) 2022-06-02T02:53:32Z 2022-06-02T02:53:32Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/158253 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Low, Xian Hao Application of machine learning techniques in vehicle collision detection |
| title | Application of machine learning techniques in vehicle collision detection |
| title_full | Application of machine learning techniques in vehicle collision detection |
| title_fullStr | Application of machine learning techniques in vehicle collision detection |
| title_full_unstemmed | Application of machine learning techniques in vehicle collision detection |
| title_short | Application of machine learning techniques in vehicle collision detection |
| title_sort | application of machine learning techniques in vehicle collision detection |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/158253 |
| work_keys_str_mv | AT lowxianhao applicationofmachinelearningtechniquesinvehiclecollisiondetection |