对自动地面车辆状态估计方法的审查:对估计目标,车辆特性和关键算法的观点
Xiaoyu Wang1,2, Te Chen3, Renzhong Wang1,2
1School of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215000, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本综述涵盖了车辆驾驶状态估计,基于估计对象,车辆特性和算法分析技术. 它提供了关于该领域研究进展和未来趋势的全面概述.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 车辆驾驶状态的估计对于先进的驾驶辅助系统 (ADAS) 和自动驾驶至关重要.
- 准确估计车辆动态和环境条件对于安全和高效的运行至关重要.
研究的目的:
- 提供车辆驾驶状态估计研究的全面审查.
- 从多个角度分析当前技术:估计对象,车辆特性和算法.
- 确定车辆状态估计的未来研究趋势.
主要方法:
- 审查关于车辆状态估计的现有文献.
- 基于估计对象 (态度,动态,环境) 的技术的分类.
- 考虑车辆特性 (合,多源数据,状态转换) 的方法分析.
- 检查关键算法:卡尔曼过,机器学习和混合方法.
主要成果:
- 在车辆状态估计中确定了关键研究领域.
- 突出了不同算法方法的优缺点.
- 展示了考虑车辆特性对于可靠估计的重要性.
结论:
- 车辆状态估计是一个快速发展的领域,对汽车安全和自主性有重大影响.
- 结合基于模型和数据驱动的方法的混合方法显示出有希望.
- 需要进一步的研究来解决复杂的车辆动态和环境不确定性.
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