以IMU为中心的移动时域估计框架用于跨车辆和抓地力条件下的横向动力学估计
IMU-Centric Moving Horizon Estimation for Lateral Dynamics Estimation Across Vehicles and Grip Conditions
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中文总结 AI 辅助
本文提出一种以IMU为中心的移动时域估计框架，利用标准车载信号重建横向速度，无需外部感知或轮胎参数调校，并在多种车辆和条件下验证了其准确性和鲁棒性。
中文摘要 AI 辅助
在附着极限附近的横向车辆动力学精确估计对于稳定性控制和高性能驾驶至关重要，但横向速度很少被直接测量，因为光学传感器等传感器成本高昂。本文提出了一种以惯性测量单元（IMU）为中心的移动时域估计框架，利用标准车载信号重建横向速度，无需依赖外部感知里程计或详细的轮胎参数调校。在人类驾驶的跑车和自动驾驶开轮赛车上的实验验证，覆盖不同赛道、操作和条件，展示了横向速度和横向加速度估计的准确性和鲁棒性。所提出的框架可在该URL获取。
英文摘要
Accurate estimation of lateral vehicle dynamics near the adhesion limit is important for stability control and high-performance driving, but lateral velocity is rarely measured directly because sensors such as optical sensors are costly. This paper presents an inertial measurement unit (IMU)-centric Moving Horizon Estimation framework that reconstructs lateral velocity using standard onboard signals, without relying on exteroceptive odometry or detailed tire-parameter tuning. Experimental validation on human-driven sports cars and an autonomous open-wheel race car across tracks, maneuvers, and conditions demonstrates accurate and robust lateral velocity and lateral acceleration estimates. The proposed framework is available at https://github.com/Aseuffo/IMU-Centric-MHE
发表机构
- University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
机构由 AI 辅助整理，请以论文原文为准。
