Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel l...
Guardado en:
Autores principales: | Jianfei Huang, Xinchun Cheng, Yuying Shen, Dewen Kong, Jixin Wang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/dbea8f638b3d4011b4bb15b459b2497a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Study on modeling and numerical analysis for the prediction of wheel wear development
por: Ariku YOSHIOKA, et al.
Publicado: (2017) -
Histone Loaders CAF1 and HIRA Restrict Epstein-Barr Virus B-Cell Lytic Reactivation
por: Yuchen Zhang, et al.
Publicado: (2020) -
Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving
por: Yonghwan Jeong, et al.
Publicado: (2021) -
Robust model predictive kinematic tracking control with terminal region for wheeled robotic systems
por: Phuong Nam Dao, et al.
Publicado: (2021) -
An Extended Car-Following Model Based on Visual Angle and Electronic Throttle Effect
por: Hongxia Ge, et al.
Publicado: (2021)