准确而实时地获取车速信息是汽车实现高精度定位与导航、高级巡航控制和编队巡航控制等功能的必要前提，文中提出一种基于UniTire轮胎模型的汽车行驶速度估计方法。首先，基于UniTire轮胎模型建立包含汽车纵向、侧向和横摆动态的车速估计名义模型，并利用自适应遗传算法的全局搜索优势和拟牛顿法的局部搜索优势辨识出UniTire轮胎模型的特征参数的全局最优解；随后，以车速估计名义模型的过程噪声和观测噪声为调度变量，利用强跟踪中心差分卡尔曼滤波器设计一簇车速估计器，并采用交互式多模型算法对基于强跟踪中心差分卡尔曼滤波器的车速估计器簇的输出结果进行平滑融合；最后，利用整车级硬件在环仿真平台对所提出的基于交互式强跟踪中心差分卡尔曼滤波器的车速估计算法的可行性和有效性进行验证。结果表明：相对于基于强跟踪中心差分卡尔曼滤波器的车速信息估计算法，本文提出的车速信息估计算法对系统噪声统计特性具有更好的自适应能力，并且具有更高的估计精度。

Accurate and real-time acquisition of vehicle speed information is a necessary prerequisite for vehicle to achieve high-precision positioning and navigation, advanced cruise control and formation cruise control. Therefore, a novel vehicle speed estimation method was proposed based on UniTire model. Firstly, a vehicle speed estimation nominal model, which includes vehicle longitudinal, lateral and yaw dynamics, was established based on UniTire model. The global accurate solution of characteristic parameters of the model was obtained based on adaptive genetic algorithm and quasi-Newton algorithm. Subsequently, the process noise and observed noise of the nominal model was taken as scheduling variables, and a cluster of vehicle speed estimators was designed based on strong tracking central difference Kalman filter. The outputs of the cluster of vehicle speed estimators were smoothly fused based on interactive multiple model algorithm. Finally, the feasibility and validity of the proposed self-driving vehicle speed estimation method were verified with hardware in the loop simulation system for full-vehicle. The results show that, compared with strong tracking central difference Kalman filter, the proposed method has stronger adaptive ability to the statistical characteristics of the system noise and higher estimation accuracy.