Driving style recognition with privacy protection
The objective of the FYP is to use a myriad of RNN, Long Short-Term Memory networks (LSTMs) to detect between different driving styles and simultaneously include a method to protect the confidentiality of the data captured. Using data collected and filtered through a Kalman Filter, to estimate the p...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/150350 |
| _version_ | 1811676276552892416 |
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| author | Seet, Jonathan Wei Han |
| author2 | Tay, Wee Peng |
| author_facet | Tay, Wee Peng Seet, Jonathan Wei Han |
| author_sort | Seet, Jonathan Wei Han |
| collection | NTU |
| description | The objective of the FYP is to use a myriad of RNN, Long Short-Term Memory networks (LSTMs) to detect between different driving styles and simultaneously include a method to protect the confidentiality of the data captured. Using data collected and filtered through a Kalman Filter, to estimate the position, without showcasing the true location. In addition, by creating a neural network that produces an encoded version of the raw data that can be utilized to be train on working models. With the encoded version of the raw data, the Extended Kalman Filter would be able to produce a hidden version of the driver’s location This would bring about privacy while having the ability to distinguish a driver’s behavior. |
| first_indexed | 2024-10-01T02:18:54Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/150350 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:18:54Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1503502023-07-07T18:21:25Z Driving style recognition with privacy protection Seet, Jonathan Wei Han Tay, Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence The objective of the FYP is to use a myriad of RNN, Long Short-Term Memory networks (LSTMs) to detect between different driving styles and simultaneously include a method to protect the confidentiality of the data captured. Using data collected and filtered through a Kalman Filter, to estimate the position, without showcasing the true location. In addition, by creating a neural network that produces an encoded version of the raw data that can be utilized to be train on working models. With the encoded version of the raw data, the Extended Kalman Filter would be able to produce a hidden version of the driver’s location This would bring about privacy while having the ability to distinguish a driver’s behavior. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-13T13:01:25Z 2021-06-13T13:01:25Z 2021 Final Year Project (FYP) Seet, J. W. H. (2021). Driving style recognition with privacy protection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150350 https://hdl.handle.net/10356/150350 en A4263-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Seet, Jonathan Wei Han Driving style recognition with privacy protection |
| title | Driving style recognition with privacy protection |
| title_full | Driving style recognition with privacy protection |
| title_fullStr | Driving style recognition with privacy protection |
| title_full_unstemmed | Driving style recognition with privacy protection |
| title_short | Driving style recognition with privacy protection |
| title_sort | driving style recognition with privacy protection |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/150350 |
| work_keys_str_mv | AT seetjonathanweihan drivingstylerecognitionwithprivacyprotection |