汽车雷达系统的自我车速校正使用卷积神经网络
Sunghoon Moon1, Daehyun Kim1, Younglok Kim1
1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了基于汽车雷达的自我车速检测网络 (AVSD Net),用于准确的速度估计. 该模型通过改进自我车辆速度校正来提高高级驾驶辅助系统的安全性.
科学领域:
- 汽车工程 汽车工程
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 对于先进的驾驶辅助系统 (ADAS) 的日益增长的需求需要强大的汽车雷达.
- 当前的雷达系统面临着对角性能和影响探测范围的安装变化的挑战.
研究的目的:
- 提出一种新的卷积神经网络模型,AVSD Net,以使用汽车雷达数据准确地估计自我车辆的速度.
- 详细介绍预处理和后处理技术,以精确纠正车辆速度.
主要方法:
- 开发使用卷积神经网络的AVSD Net模型.
- 将范围-速度频谱数据输入到AVSD Net模型中.
- 实施详细的预处理和后处理以纠正速度.
主要成果:
- 该AVSD网络模型有效地估计自我车辆的速度,独立于雷达的角度性能和安装角度.
- 该方法可以减少检测范围的损失,而无需专门的光束要求.
- 预处理和后处理实现了精确的速度校正,并降低了模型复杂度.
结论:
- 拟议的AVSD Net模型为汽车雷达系统中自我车速估计提供了有效的解决方案.
- 该方法的稳定性和复杂性降低使其适用于嵌入式系统.
- 这种速度校正技术显著提高了ADAS应用中的安全功能.
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