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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Bibliographic Details
Main Author: Seet, Jonathan Wei Han
Other Authors: Tay, Wee Peng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/150350
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Seet, Jonathan Wei Han
Driving style recognition with privacy protection
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.
author2 Tay, Wee Peng
author_facet Tay, Wee Peng
Seet, Jonathan Wei Han
format Final Year Project
author Seet, Jonathan Wei Han
author_sort Seet, Jonathan Wei Han
title Driving style recognition with privacy protection
title_short 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_sort driving style recognition with privacy protection
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150350
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