期刊
SENSORS
卷 21, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/s21041282
关键词
sensor fusion; state estimation; vehicle dynamics; convolutional neural network; dual extended Kalman filter; vehicle roll and pitch angle; vehicle acceleration and angular velocity
资金
- Hyundai Motor Group Academy Industry Research Collaboration
This paper proposes a method for vehicle attitude estimation using a convolutional neural network and a dual-extended Kalman filter. Experimental results show that this method can accurately estimate the vehicle's attitude and acceleration information, improving modeling accuracy.
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.
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