在汽车超声波周围传感中使用紧的2 × 2传感器阵列和卷积神经网络进行对象分类
Jona Eisele1,2, André Gerlach1, Yannik Manz1,3
1Robert Bosch GmbH, Corporate Research, Robert-Bosch-Campus 1, Renningen, 71272, Germany.
The Journal of the Acoustical Society of America
|April 7, 2025
概括
这项研究引入了一个2x2阵列超声波传感器,以改善公园试点系统的对象分类. 这种增强的传感器提供方向信息,大大提高了对传统单元传感器的准确性.
科学领域:
- 汽车工程 汽车工程
- 传感器技术 传感器技术
- 机器学习 机器学习
背景情况:
- 目前的公园试点系统依赖于超声波周围传感器,受单元传感器性能限制.
- 对象分类对于先进的驾驶员辅助系统至关重要,但由于传统超声波传感器缺乏方向数据而受到阻碍.
研究的目的:
- 为了提高超声波周围传感中的对象分类准确性.
- 与单元传感器相比,评估一个小型2x2阵列超声波传感器的有效性.
主要方法:
- 建议用2x2阵列传感器取代单元超声波传感器.
- 开发了一种高效的卷积神经网络 (CNN),用于分类预处理的传感器信号.
- 评估的特征提取方法包括光束成形和声源地图.
主要成果:
- 2x2阵列传感器与CNN和声源地图相结合,实现了有希望的分类准确度.
- 拟议的阵列传感器系统在对象分类方面显著超过传统单元传感器.
- 结合来自阵列传感器的方向信息,增强了目标区分.
结论:
- 小孔阵列传感器在超声波周围传感中为对象分类提供了显著的改进.
- 利用定向信息是提高超声波感知系统性能的关键.
- 开发的CNN方法有效地分类来自阵列传感器的信号,展示了实际应用的潜力.
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