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IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics ?

机译:基于IMU的车辆动力学自动车辆滑移角和姿态估计

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The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.
机译:滑移角和姿态对于自动驾驶至关重要。提出了一种基于系统惯性测量单元(IMU)的车辆动力学辅助的车辆滑移角和姿态估计方法。该方法可以同时自动地估计滑移角和姿态。以正确的姿态,即使基于车辆动力学模型(VDM)的速度估算器在短时间内发散,也可以精确估算出滑移角。首先,考虑到IMU和旋转中心之间的杠杆臂,由两个估计器基于VDM估计纵向速度,俯仰角,横向速度和侧倾角。当此信息具有很高的保真度时,将其用于帮助基于IMU的滑移角和姿态估计器正确消除累积的误差。由于在关键转向过程中检测基于VDM的估计器的异常估计结果存在时间延迟,因此提出了一种新颖的延迟估计和预测结构,以避免基于IMU的滑移角和姿态估计器的车辆动力学估计器产生异常反馈。最后,所提出的估计方法在包括双车道变更(DLC)和激流回旋动作的大型横向激励实验测试中得到了验证。

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