杠杆(统计)
车辆动力学
控制理论(社会学)
航程(航空)
人工神经网络
残余物
刚度
理论(学习稳定性)
工程类
传感器融合
计算机科学
非线性系统
估计理论
软件
跟踪(教育)
算法
控制系统
卡尔曼滤波器
人工智能
摩擦系数
融合
计算机视觉
控制工程
运动估计
跟踪误差
模拟
制导系统
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2025-10-01
卷期号:75 (4): 5592-5607
标识
DOI:10.1109/tvt.2025.3616636
摘要
The road adhesion coefficient is a critical parameter in active safety control systems. However, achieving both accuracy and real-time performance in estimating this coefficient for nonlinear vehicle-tire systems remains a significant challenge. To address this issue, this paper proposes an online fusion method for estimating the road adhesion coefficient that integrates image-based and vehicle dynamic data. First, a threedegree- of-freedom vehicle dynamic model is employed, and correction factors are introduced into the Dugoff tire model to enable adaptive cornering stiffness calculation. Next, to leverage visual information effectively, the range of the road adhesion coefficients obtained from a residual network is incorporated as the upper and lower bounds of constraints in the moving horizon estimation method. Additionally, a detailed stability proof for the moving horizon estimation method is provided. Finally, the effectiveness of the proposed estimation method is validated through both software simulation and vehicle tests. The results demonstrate that the proposed estimation method achieves high estimation accuracy and rapid convergence.
科研通智能强力驱动
Strongly Powered by AbleSci AI