针对越野环境下路面多变导致车辆纵向动力学控制性能恶化的问题，提出一种融合扩展卡尔曼滤波（Extended Kalman Filter, EKF）与随机森林（Random Forest, RF）的自适应控制策略。该策略采用多模态感知框架，先利用 RF 算法实时识别路面类型，再将结果作为 EKF 估计器的先验知识，实现对路面峰值附着系数的精准估计。验证表明，该策略能有效适应不同路面，在高附与低附路面上均能显著抑制车轮过度滑移，将滑移率稳定于最优区间，有利于提升车辆加速与制动性能，缩短制动距离，增强越野行驶的安全性。

Aiming at the problem of deterioration of vehicle longitudinal dynamic control performance caused by variable road surfaces in off-road environments, an adaptive control strategy integrating Extended Kalman Filter(EKF) and Random Forest (RF) is proposed. This strategy adopts a multimodal perception framework. Firstly, it uses the RF algorithm to identify the road surface type in real time, and then takes the result as the prior knowledge of the EKF estimator to achieve precise estimation of the peak adhesion coefficient of the road surface. Verification shows that this strategy can effectively adapt to different road surfaces. It can significantly suppress excessive wheel slippage on both high and low adhesion roads, stabilize the slip ratio within the optimal range, which is conducive to improving the vehicle's acceleration and braking performance, shortening the braking distance, and enhancing the safety of off-road driving.