Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박장현 | - |
dc.date.accessioned | 2022-12-05T05:23:05Z | - |
dc.date.available | 2022-12-05T05:23:05Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | SENSORS, v. 21, NO. 4, article no. 1282 | en_US |
dc.identifier.issn | 1424-8220;1424-3210 | en_US |
dc.identifier.uri | https://www.mdpi.com/1424-8220/21/4/1282 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/177941 | - |
dc.description.abstract | The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle's chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates. | en_US |
dc.description.sponsorship | This work was supported by the Hyundai Motor Group Academy Industry Research Collaboration. | en_US |
dc.language | en | en_US |
dc.publisher | MDPI | en_US |
dc.source | 80097_박장현.pdf | - |
dc.subject | sensor fusion | en_US |
dc.subject | state estimation | en_US |
dc.subject | vehicle dynamics | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | dual extended Kalman filter | en_US |
dc.subject | vehicle roll and pitch angle | en_US |
dc.subject | vehicle acceleration and angular velocity | en_US |
dc.title | Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 21 | - |
dc.identifier.doi | 10.3390/s21041282 | en_US |
dc.relation.journal | SENSORS | - |
dc.contributor.googleauthor | Ok, Minseok | - |
dc.contributor.googleauthor | Ok, Sungsuk | - |
dc.contributor.googleauthor | Park, Jahng Hyon | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 미래자동차공학과 | - |
dc.identifier.pid | jpark | - |
dc.identifier.orcid | https://orcid.org/0000-0002-4308-2910 | - |
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