Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

轮胎-道路摩擦系数（TRFC）的知识对于自动驾驶汽车的控制和主动安全系统的设计非常有价值。本文研究基于纵向车辆动力学的TRFC估计。提出了一种两阶段的TRFC估计方案，该方案限制了对车辆运动的干扰。在第一阶段中设计了一系列的制动压力脉冲，以识别所需的最小脉冲压力，以便在对车辆运动产生最小干扰的情况下可靠地估算TRFC。此阶段还提供TRFC的定性估计。在第二阶段，根据测量信号直接计算轮胎法向力和滑移率，开发了基于车轮旋转动力学的修正力观测器，用于估算轮胎制动力。随后提出了一种受限的无味卡尔曼滤波器（CUKF）算法来识别TRFC，以实现快速收敛和增强的估计精度。通过CarSim™-MATLAB / Simulink™联合仿真评估了提出的方法的有效性，该仿真考虑了在高，中，低摩擦的道路上以不同速度行驶时的车辆运动。结果表明，所提出的两阶段方法可以产生对道路摩擦的准确估计，而对车速的影响相对较低。