Name
Title | ||
|---|---|---|
Estimation Of Vehicle Attitude, Acceleration, And Angular Velocity Using Convolutional Neural Network And Dual Extended Kalman Filter |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21041282 | SENSORS |
Keywords | DocType | Volume |
sensor fusion, state estimation, vehicle dynamics, convolutional neural network, dual extended Kalman filter, vehicle roll and pitch angle, vehicle acceleration and angular velocity | Journal | 21 |
Issue | ISSN | Citations |
4 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minseok Ok | 1 | 0 | 0.34 |
| Sungsuk Ok | 2 | 0 | 0.34 |
| Jahng Hyon Park | 3 | 0 | 0.34 |