前方碰撞预警策略基于毫米波雷达和视觉融合
Chenxu Sun1, Yongtao Li1, Hanyan Li2
1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究引入了使用增强视觉和雷达算法改进的前方碰撞预警 (FCW) 系统. 新的决策级核聚变战略在具有挑战性的驾驶条件下显著减少了虚假和错过的警报.
科学领域:
- 汽车工程 汽车工程
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 前方碰撞预警系统 (FCW) 对道路安全至关重要.
- 现有的多传感器聚变方法在不利条件下扎着高错误报警率.
研究的目的:
- 为FCW系统制定一个新的决策级核聚变碰撞预警策略.
- 加强雷达跟踪和视觉检测算法,以最大限度地减少错误和错过的警报.
主要方法:
- 具有基于信息的记忆指数的自适应卡尔曼过器用于雷达目标跟踪.
- 一个增强的YOLOv5s模型,包含一个选择性内核和瓶注意力机制 (SKBAM),用于改进车辆检测.
- 一个决策级核聚变战略,将毫米波雷达和视觉数据与最低安全距离模型相结合.
主要成果:
- 拟议的算法显示,虚假报警率减少了11.619%.
- 与传统方法相比,错过警报率减少了15.672%.
- 在各种天气和道路条件中验证了有效的性能.
结论:
- 开发的决策级融合战略显著提高了FCW系统的可靠性.
- 改进的雷达和视觉算法有助于减少错误和错过的警报.
- 该方法为复杂环境中的前置碰撞预警提供了更强大的解决方案.
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