Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박장현 | - |
dc.date.accessioned | 2022-10-31T02:30:59Z | - |
dc.date.available | 2022-10-31T02:30:59Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | SENSORS, v. 21, no. 4, article no. 1282, page. 1-20 | en_US |
dc.identifier.issn | 1424-8220 | 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/20.500.11754/176180 | - |
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.subject | sensor fusion; state estimation; vehicle dynamics; convolutional neural network; dual extended Kalman filter; vehicle roll and pitch angle; 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.page | 1-20 | - |
dc.relation.journal | SENSORS | - |
dc.contributor.googleauthor | Ok, Minseok | - |
dc.contributor.googleauthor | Ok, Sungsuk | - |
dc.contributor.googleauthor | Park, Jahng Hyon | - |
dc.relation.code | 2021006182 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF AUTOMOTIVE ENGINEERING | - |
dc.identifier.pid | jpark | - |
dc.identifier.orcid | https://orcid.org/0000-0002-4308-2910 | - |
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