【摘要】 路侧感知是云控车路协同感知的组成部分。为提升路侧传感器的感知精度和稳定性,该文提出了一种基于量测噪声自适应扩展Kalman滤波器(AEKF)的路侧多传感器融合方法。基于路侧相机、激光雷达、毫米波雷达的感知结果,实现了异质传感器目标级数据的融合。采用一种量测噪声在线获取方法,检测了传感器测量值的稳定性,生成了量测噪声的修正系数,自适应调整了量测噪声;经过了实车试验。结果表明:相较于单传感器,采用该多传感器融合方法使横向距离估计精度提高9.7%,纵向距离估计精度提高5.4%,速度估计精度提高26.6%;由于该算法的横向距离估计精度提高44.9%,纵向距离估计精度提高21.3%,速度估计精度提高64.4%;因此,该文AEKF算法的估计精度高于传统扩展Kalman滤波算法(EKF)的精度。更多还原

【Abstract】 Roadside perception is a component of cloud control cooperative vehicle infrastructure perception. This paper proposed a fusion method for roadside multi-sensors based on measurement noise Adaptive Extended Kalman Filter(AEKF) to improve the perception accuracy and stability of roadside sensors. The fusion of target-level data from heterogeneous sensors was realized based on the sensing results of roadside cameras, lidars, and millimeter-wave radars. An online acquisition method of measurement noise was used to detect the stability of the sensor measurement value, to generate correction coefficients for the measurement noise, and to adjust adaptively the measurement noise. This method was tested in a real vehicle. The results show that this multi-sensor fusion method improves the accuracy of lateral distance estimation by 9.7%, the longitudinal distance estimation accuracy by 5.4%, and the speed estimation accuracy by 26.6%, compared with a single sensor; With the estimation accuracy of the traditional Extended Kalman Filter(EKF) algorithm being improved by 44.9% in the horizontal distance, by 21.3% in the longitudinal distance, and by 64.4% in the speed; Therefore, the AEKF algorithm estimation accuracy of this paper is higher than that of the traditional EKF algorithm.更多还原