合作自动驾驶汽车的高效多传感器融合利用C-ITS基础设施和机器学习
Jiwon Kwak1, Hayoung Jeon2, Seokil Song2
1School of Cbersecurity, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了合作智能运输系统 (C-ITS) 的新型两阶段传感器融合框架. 该方法通过有效处理来自多个传感器的杂,异步数据,提高了轨迹精度和实时性能.
科学领域:
- 运输工程 运输工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 合作智能运输系统 (C-ITS) 需要强大的传感器融合来实现实时数据处理.
- 在C-ITS中的基础设施传感器通常会产生噪音和异步数据.
- 现有的融合策略在处理数据变化和计算负载方面面临挑战.
研究的目的:
- 为C-ITS应用提出一个新的两阶段数据融合框架.
- 在动态环境中提高传感器数据融合的准确性和效率.
- 为了应对噪音,异步和多传感器数据集成的挑战.
主要方法:
- 一个两阶段的数据融合框架,结合了基于网格的索引方法和光梯度增强机 (LGBM),并增加了扩展卡尔曼波器 (EKF).
- 第一个阶段:混合EKF-LGBM模型用于降低噪音,改进轨迹和传感器流同步.
- 第二阶段:基于网格的索引,以实现高效的对象关联和合并多传感器测量.
主要成果:
- 拟议的框架显示了近实时性能和改进的轨迹精度之间的平衡.
- 在噪音等级为1的无香料卡尔曼波器 (UKF) 性能优于它,速度提高1.81倍.
- 现实世界的测试显示,根平均平方误差 (RMSE) 比基线测量改进了1.54倍.
- 有效地过噪音,并减少计算开销,以实现实际的C-ITS可行性.
结论:
- 开发的两阶段传感器融合框架对C-ITS应用有效.
- 这种方法在速度和准确性方面显著提高了数据处理能力.
- 该系统为集成智能运输系统中的多传感器数据提供了实用和高效的解决方案.
关键词:
在C-ITS中,C-ITS可以使用C-ITS.LGBM LGBM 在线观看自动驾驶自动驾驶的自动驾驶.融合 融合 融合 融合 融合 融合 融合 融合 融合 融合机器学习是机器学习.多传感器多传感器更多相关视频
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