Tan Chenkai, Cai Yingfeng, Wang Hai, Sun Xiaoqiang, Chen Long
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2023 Jul 25;23(15):6665. doi: 10.3390/s23156665.
This paper proposes a novel vehicle state estimation (VSE) method that combines a physics-informed neural network (PINN) and an unscented Kalman filter on manifolds (UKF-M). This VSE aimed to achieve inertial measurement unit (IMU) calibration and provide comprehensive information on the vehicle's dynamic state. The proposed method leverages a PINN to eliminate IMU drift by constraining the loss function with ordinary differential equations (ODEs). Then, the UKF-M is used to estimate the 3D attitude, velocity, and position of the vehicle more accurately using a six-degrees-of-freedom vehicle model. Experimental results demonstrate that the proposed PINN method can learn from multiple sensors and reduce the impact of sensor biases by constraining the ODEs without affecting the sensor characteristics. Compared to the UKF-M algorithm alone, our VSE can better estimate vehicle states. The proposed method has the potential to automatically reduce the impact of sensor drift during vehicle operation, making it more suitable for real-world applications.
本文提出了一种新颖的车辆状态估计（VSE）方法，该方法将物理信息神经网络（PINN）和流形上的无迹卡尔曼滤波器（UKF-M）相结合。这种VSE旨在实现惯性测量单元（IMU）校准，并提供有关车辆动态状态的全面信息。所提出的方法利用PINN通过用常微分方程（ODE）约束损失函数来消除IMU漂移。然后，使用UKF-M通过六自由度车辆模型更准确地估计车辆的三维姿态、速度和位置。实验结果表明，所提出的PINN方法可以从多个传感器进行学习，并通过约束ODE来减少传感器偏差的影响，而不会影响传感器特性。与单独的UKF-M算法相比，我们的VSE能够更好地估计车辆状态。所提出的方法有可能在车辆运行期间自动减少传感器漂移的影响，使其更适合实际应用。
