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Updated: Jan 7, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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使用深度强化学习结合无气味卡尔曼波器的车辆侧滑角度估计

Liguang Wu1,2, Wei Wang2, Penghui Li3

  • 1School of Mechanical Engineering, Tianjin University, Tianjin 300192, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
概括

这项研究引入了一种新的方法,将无气味卡尔曼波器 (UKF) 与深度增强学习 (DRL) 结合起来,以准确地估计车辆侧滑角度. 该方法动态优化噪声参数,在复杂的驾驶条件下显著提高准确性和稳定性.

关键词:
深度强化学习的学习.国家估计估计.没有香味的卡尔曼过器.车辆动态 车辆动态车辆侧面的滑坡角度

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