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基于RSU和车载摄像机融合的障碍物检测方法
Shaohong Ding1, Yi Xu1,2, Qian Zhang3
1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的道路障碍物检测系统,将路边设备和车辆摄像头融合在一起. 新方法提高了智能辅助驾驶的通用障碍物识别准确性和速度.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
- 机器人技术和自主系统
背景情况:
- 道路障碍物检测对于智能辅助驾驶技术至关重要.
- 现有的方法往往忽视了通用的障碍物检测,影响了安全.
- 一般化的障碍物包括伪障碍物和低于车辆最大通行高度的物体.
研究的目的:
- 提出和验证一种新的障碍物检测方法,将路边装置 (RSU) 和车载摄像头结合起来.
- 为了提高通用障碍物分类的准确性,并减少空间检测的复杂性.
- 提高在复杂的驾驶环境中获取障碍信息的可靠性.
主要方法:
- 一种混合方法,将基于视觉-惯性测量单位 (IMU) 的方法 (VIDAR) 与RSU背景差异方法集成在一起.
- VIDAR利用相机-IMU融合进行对象识别和距离测定,通过反向视角转换计算对象高度.
- 通过用户数据协议 (UDP) 从车辆传输数据到 RSU,以消除伪障碍和减少错误.
主要成果:
- 拟议的方法比比较方法 (VIDAR,基于RSU的,YOLOv5) 实现了2.3%,17.4%和1.8%的精度改善.
- 与基于RSU的方法相比,障碍物检测速度提高了1.1%.
- 证明了检测范围的扩大和有效消除虚假的障碍信息.
结论:
- 合并的RSU-车辆摄像头方法显著改善了通用道路障碍物检测.
- 视达技术有效地解决了获得准确障碍信息的挑战.
- 该方法提供了一个强大而高效的解决方案,以提高智能辅助驾驶的道路安全.
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