针对无人驾驶汽车容错控制对速度信息软测量技术的需求，提出了一种基于交 互式多模型无迹卡尔曼滤波器的无人驾驶汽车速度估计方法，以自适应系统未知的噪声 统计特性。首先，基于无人驾驶汽车定位信息建立了包含汽车运动学和动力学特性的名 义模型，并采用前向欧拉离散化方法将其转化为包含系统噪声统计特性的状态空间名义 模型; 然后，采用一系列典型值描述系统未知的噪声统计特性，得到一系列包含不同系 统噪声统计特性的状态空间名义模型，并针对每一个状态空间名义模型，分别采用无迹 卡尔曼滤波器对无人驾驶汽车的速度进行实时估计，通过交互式多模型算法平滑融合无 迹卡尔曼滤波器的输出，由此得到对系统噪声统计特性具有自适应能力的交互式多模型 无迹卡尔曼滤波器。实车试验结果表明，所提出的方法对汽车纵向速度的估计精度是传 统无迹卡尔曼滤波方法的 4 倍，对汽车侧向速度的估计精度是传统无迹卡尔曼滤波方法 的 1. 5 倍，满足无人驾驶汽车容错控制的需求。

According to the requirement of the fault-tolerant control for the soft speed-sensing technology，a novel self-driving vehicle speed estimation method based on the interacting multiple-model unscented Kalman filter was proposed to adapt to the unknown statistical characteristics of the system noise． Firstly，a nominal model，which includes vehicle kinematic and dynamic characteristics，was established based on the positioning information of the self-driving vehicle，and then it was transformed into a state space nominal model including the unknown statistical characteristics of the system noise by using the forward Euler discretization method． Secondly，a series of typical values were used to describe the unknown statistical characteristics of the system noise，and a series of state space nominal models including different statistical characteristics of the system noise were obtained． For each state space nominal model，unscented Kalman filter was used to estimate the self-driving vehicle speed and all of the outputs were smoothly fused by interactive multiple-model algorithm． Thus，the interacting multiple-model unscented Kalman filter with adaptive ability to the statistical characteristics of the system noise was obtained． Simulation results show that the estimation accuracy of the proposed method for the vehicle longitudinal and lateral speeds is 4 times and 1. 5 times as many as that of the unscented Kalman filter，respectively，which satisfies the requirement of the fault-tolerant control for the self-driving vehicle．