使用自动驾驶汽车的增强GDM和RLS实时估计轮胎度
Zhenyu Qin1, Jiaqi Wang2, Panxue Liu3
1School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China.
Scientific reports
|August 29, 2025
概括
准确估计轮胎侧面刚度 (TLS) 对自动驾驶汽车的安全至关重要. 灵感来自深度学习的新型梯度下降方法 (GDM) 提供了改进的TLS实时跟踪,增强了车辆控制.
科学领域:
- 车辆动力学和控制
- 工程应用中的机器学习
- 汽车安全系统
背景情况:
- 曲线稳定性对于车辆的横向控制至关重要,因为它受到非线性轮胎与道路相互作用的严重影响.
- 轮胎侧向刚度 (TLS) 是影响向稳定的关键参数,因轮胎和道路状况而有所不同.
- 精确的TLS估计对于自动驾驶安全至关重要,特别是在苛刻的场景中.
研究的目的:
- 提出一个新的轮胎侧面度 (TLS) 识别框架.
- 通过深度学习来探索修改后的梯度下降方法 (GDM) 进行TLS估计.
- 开发和评估用于TLS跟踪的改进实时算法.
主要方法:
- 建立了递归最小方程 (RLS) 和GDM之间的理论联系,确定了RLS作为GDM的特定案例.
- 为实时TLS识别开发了改进的RLS变种.
- 在不同的条件下使用模拟来比较各种GDM和RLS算法,包括像Adam这样的自适应方法.
主要成果:
- 在不同的算法和不同的条件下展示了有效的TLS跟踪能力.
- 适应性GDM方法,如Adam,在追踪TLS方面表现优越.
- 通过适应性方法实现相对稳定状态误差 (RSSE) 低于5%,响应时间 (t10) 低于3秒.
结论:
- 拟议的GDM灵感框架有效地实时识别TLS.
- 像Adam这样的自适应算法为自动驾驶汽车的TLS估计提供了更高的性能.
- 结果为在安全关键的自动驾驶系统中选择适当的估计器提供了实用见解.
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