Chair of Mechatronics, Faculty of Engineering, University of Duisburg-Essen, 47051 Duisburg, Germany.
Sensors (Basel). 2022 May 5;22(9):3513. doi: 10.3390/s22093513.
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.
车辆中虚拟传感器的使用是安装物理硬件的一种具有成本效益的替代方案。除了基于理论建模的物理模型外，人工智能和机器学习方法也越来越多地被应用，这些方法包含实验建模。由于由此产生的黑盒特性，基于人工智能的虚拟传感器并不完全可靠，这在安全关键型应用中可能会产生致命后果。因此，提出了一种混合方法来保证基于人工智能的估计的可靠性。应用示例是车辆侧倾角度的状态估计。该状态估计与中央预测车辆动力学控制相耦合。通过 IPG CarMaker 和 MATLAB/Simulink 之间的协同仿真来执行和验证。通过使用混合方法，可以检测和处理由于输入信号错误而导致人工智能模型产生的不可靠估计。因此，始终可以获得有效和可靠的状态估计。
