In recent years, many unmanned vehicle designs and autonomous driving functions have been introduced in the automotive industry to increase the safety and versatility of multiple vehicle designs. It is critical to select a plant vehicle model and a compact uncertainty representation regardless of the vehicle scale for vast deployment. This article introduces a dimensionless representation of a dynamic vehicle model that is suitable for generalized dynamic analysis. Demonstrations included in this article showed that the compact uncertainty bounds of the dimensionless parameters could be used to generate an adaptive observer suitable for full-sized and corresponding scaled vehicles. The proposed dimensionless model-based observer can assess motion states, mass, center of gravity displacement, and the tire stiffness online with common onboard inertial measurement unit (IMU). The effectiveness and sensitivity of the proposed technique are highlighted through simulations and experiments on scaled test vehicles.

中文翻译：

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增强卡尔曼滤波的多尺度无人飞行器基于模型的无量纲系统跟踪

近年来，在汽车工业中引入了许多无人驾驶车辆设计和自动驾驶功能，以提高多种车辆设计的安全性和多功能性。无论大规模部署的车辆规模如何，选择工厂车辆模型和紧凑的不确定性表示形式都是至关重要的。本文介绍了适用于广义动态分析的动态车辆模型的无量纲表示。本文包括的演示表明，无量纲参数的紧凑不确定性边界可用于生成适用于全尺寸和相应比例缩放的车辆的自适应观测器。提出的基于模型的无量纲观察者可以评估运动状态，质量，重心位移，以及在线轮胎惯性测量单元（IMU）。通过在比例测试车辆上的仿真和实验，突出了所提出技术的有效性和敏感性。