A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles

A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles
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一种同时估计车辆侧滑和偏航角的可靠融合方法

DOI:
10.1109/tvt.2015.2496969
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发表时间:
2016-06
影响因子:
6.8
通讯作者:
Wang Yu
Wang Yu
中科院分区:
计算机科学2区
文献类型:
--
作者:
Li Xu;Chan Chingyao;Wang Yu

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车辆侧滑角和横摆角对于许多车辆安全系统是关键的。虽然已经提出了很多研究,以获得他们单独的,同时准确的估计,他们的基础上负担得起的传感器的陆地车辆应用,很少有报道。本文提出了一种融合方法,将单频双天线全球定位系统(DA-GPS)与其他低成本的车载传感器,以实现可靠的车辆侧滑角和偏航角的估计。所提出的方法采用了一种混合分散过滤架构。首先,车辆状态估计器(VSE)开发作为一个虚拟的传感器来估计车辆状态信息,主要是根据车辆动力学。它是由两个并行的扩展卡尔曼滤波器(EKF)。通过两个EKF的相互作用,VSE可以适应轮胎-路面摩擦力的变化,准确地估计车辆侧倾角。然后,根据车辆运动学,设计了一种基于联邦滤波算法的全局联邦估计器(GFE),实现了惯性传感器、DA-GPS和VSE的全局融合。特别是,提出了一种自适应推理机制,并引入到GFE,以适应复杂的驾驶情况下,如GPS故障,各种驾驶演习等,最后,所提出的方法进行了评估,通过密集的模拟和实验。总体结果表明,所提出的方法可以提供可靠的估计两个角度下的广泛的驾驶情况。
Vehicle sideslip and yaw angles are critical for many vehicle safety systems. Although much research has been presented to obtain them individually, simultaneous accurate estimation of them, based on affordable sensors for land vehicle applications, is seldom reported. This paper proposes a fusion methodology for integrating a single-frequency double-antenna Global Positioning System (DA-GPS) with other low-cost in-vehicle sensors to achieve reliable estimation of both vehicle sideslip and yaw angles. The proposed methodology adopts a hybrid decentralized filtering architecture. First, a vehicle state estimator (VSE) is developed as a virtual sensor to estimate vehicle state information, mainly according to vehicle dynamics. It is composed of two parallel extended Kalman filters (EKFs). Through the interaction of two EKFs, the VSE can adapt to the variations in tire-road friction and accurately estimate the vehicle roll angle. Then, according to vehicle kinematics, a global federated estimator (GFE) is designed, based on the federated filtering algorithm, to achieve the global fusion of inertial sensors, DA-GPS, and VSE. In particular, an adaptive inference mechanism is proposed and introduced into the GFE to adapt to complex driving situations, such as GPS failure, various driving maneuvers, etc. Finally, the proposed method is evaluated via intensive simulations and experiments. The overall results show that the proposed methodology can provide reliable estimation of two angles under a wide range of driving situations.
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