多源信息融合用于智能汽车的环境感知,使用Sage-Husa适应式扩展卡尔曼过
Yibo Meng1, Huifang Kong1, Tiankuo Liu1
1State Key Laboratory of High-Efficiency and High-Quality Conversion for Electric Power, Hefei University of Technology, Hefei 230002, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究介绍了针对智能驾驶系统的改进的自适应卡尔曼过算法. 新方法通过适应变化的传感器性能,减少位置错误,提高了多源信息融合的准确性.
科学领域:
- 智能运输系统 智能运输系统
- 机器人技术和自主系统
- 传感器融合和感知感知.
背景情况:
- 智能驾驶技术依赖于多源信息融合来进行环境感知.
- 环境变化可能会改变传感器性能,导致融合偏差.
- 现有的自适应卡尔曼过方法可能无法完全解决这些动态变化.
研究的目的:
- 为智能驾驶提出一个改进的多源信息融合算法.
- 在不同的环境条件下提高传感器融合的适应性和准确性.
- 为了减轻由传感器性能波动引起的融合偏差.
主要方法:
- 使用车辆动力学和传感器测量模型构建了一个多源信息融合系统.
- 开发了Sage-Husa自适应色扩展卡尔曼过 (SHAFEKF) 算法.
- 在Sage-Husa自适应扩展卡尔曼过 (SHAEKF) 算法中引入了色因子,以优先考虑最近的数据.
主要成果:
- 提出的SHAFEKF算法在两个场景中实现了0.137和0.071的位置平均误差.
- 与SHAEKF算法相比,定位平均误差减少了2.8%和13.4%.
- 平均平方误差分别减少了64%和72%,表明准确性和稳定性得到了改善.
结论:
- 在多源信息融合中,SHAFEKF算法表现出高精度和低波动.
- 该算法有效地提高了智能驾驶感知系统的适应性.
- 色因子在动态环境条件下显著提高了聚变性能.
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