基于多传感器信息融合的汽车行驶状态估计

State Estimation of Vehicle Based on Multi-sensors Information Fusion

为了保证汽车的主动安全控制,需要准确地估计车辆行驶状态信息。针对目前汽车状态估计中由于技术条件限制和成本过高造成的部分参数无法测量或不易测量的问题,本文中利用低成本传感器,基于信息融合技术进行汽车行驶状态估计。建立了包括横摆、横向和纵向的3自由度非线性汽车动力学模型,同时为降低噪声对系统影响,建立了自适应无迹卡尔曼滤波(AUKF)的信息融合算法,给出车辆状态最小方差意义下的融合结果。利用纵向加速度、侧向加速度和转向盘转角等低成本传感器信号融合得到所需的难以测量的质心侧偏角、横摆角速度和纵向车速。通过Matlab/Simulink-CarSim联合仿真和实车试验对所研究的估计算法进行了试验验证。试验结果表明:该算法能够准确地估计汽车质心侧偏角、横摆角速度和纵向车速,且相比于无迹卡尔曼滤波(UKF),本算法提高了估计精度和实时性。

To ensure vehicle adaptive safety control,it is necessary to accurately estimate the vehicle driving state information.For some parameters,it is difficult to measure directly for both technical and economic reasons.This paper proposes an information fusion estimation strategy using low-cost sensors to estimate vehicle driving state.A 3 DOF nonlinear vehicle dynamics model including yaw,lateral and longitudinal directions is established.In order to reduce the impact of noise on the system,an adaptive unscented Kalman filter(AUKF)information fusion algorithm is established,and the fusion result in the sense of minimum variance of vehicle state is given.Low-cost sensors signal such as longitudinal acceleration,lateral acceleration and steering wheel angle is fused to obtain the required hard-to-measure sideslip angle of mass center,yaw angular velocity and longitudinal vehicle speed.The estimation algorithm is validated by the joint simulation of Matlab/Simulink-CarSim and the real vehicle test.The test results show that the algorithm can accurately estimate the sideslip angle,yaw angular velocity and longitudinal vehicle speed.Compared with UKF,the algorithm improves the estimation accuracy and real-time performance.