Autonomous electric vehicles (AEVs) are equipped with numerous advanced control systems that rely on measurements of longitudinal velocity, yaw rate, lateral speed, and sideslip angle. However, one of the main challenges is that mass-produced vehicles cannot accommodate overly expensive sensors. This paper proposes a novel hybrid physics-data driven observer (HPDD-Observer) for vehicle dynamics state estimation (VDSE). HPDD-Observer aims to provide comprehensive and cost-effective information about the vehicle dynamics state using low-cost onboard sensors with high sampling frequency. This approach leverages the power of hybrid modeling. Firstly, it creates a linear relationship between the estimated states and the sensor vector using ridge regression. Secondly, it designs a Long Short-Term Memory (LSTM) network with dual stage attention mechanism as the data-driven component. Then, it integrates the output of ridge regression with the data-driven component, enhancing the accuracy and reliability of the deep learning model with the scientific knowledge of the physics model. Lastly, the proposed HPDD-Observer was validated through simulation tests using MATLAB/Simulink and CarSim software, followed by real-world vehicle testing. Experimental results validate that the proposed HPDD-Observer effectively combines the strengths of deep learning and physics models without any adverse effects.

中文翻译：

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一种用于车辆动力学状态估计的混合物理-数据驱动方法


自动驾驶电动汽车 （AEV） 配备了许多先进的控制系统，这些系统依赖于纵向速度、偏航率、横向速度和侧滑角的测量。然而，主要挑战之一是量产车辆无法容纳过于昂贵的传感器。本文提出了一种用于车辆动力学状态估计 （VDSE） 的新型混合物理数据驱动观测器 （HPDD-Observer）。HPDD-Observer 旨在使用具有高采样频率的低成本车载传感器提供有关车辆动力学状态的全面且经济高效的信息。这种方法利用了混合建模的强大功能。首先，它使用岭回归在估计的状态和传感器向量之间创建线性关系。其次，它设计了一个以双阶段注意力机制作为数据驱动组件的长短期记忆 （LSTM） 网络。然后，它将岭回归的输出与数据驱动组件集成，利用物理模型的科学知识提高深度学习模型的准确性和可靠性。最后，通过使用 MATLAB/Simulink 和 CarSim 软件的仿真测试验证了所提出的 HPDD-Observer，然后进行了实际车辆测试。实验结果验证了所提出的 HPDD-Observer 有效地结合了深度学习和物理模型的优势，而没有任何不利影响。