Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤상원 | - |
dc.date.accessioned | 2022-09-22T00:15:16Z | - |
dc.date.available | 2022-09-22T00:15:16Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | IEEE ACCESS, v. 8, page. 214616-214624 | en_US |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9272298 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/173157 | - |
dc.description.abstract | This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as anti-lock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant 2020R1F1A1069925 and Grant 2020R1A4A4079701. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Adaptive filter; deep neural network; slip ratio; vehicle speed estimation | en_US |
dc.title | Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles | en_US |
dc.type | Article | en_US |
dc.relation.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3040733 | en_US |
dc.relation.page | 214616-214624 | - |
dc.relation.journal | IEEE ACCESS | - |
dc.contributor.googleauthor | Kim, Jong Han | - |
dc.contributor.googleauthor | Yoon, Sang Won | - |
dc.relation.code | 2020045465 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF AUTOMOTIVE ENGINEERING | - |
dc.identifier.pid | swyoon | - |
dc.identifier.orcid | https://orcid.org/0000-0002-0201-8031 | - |
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