在降雪情况下开发使用雪轮胎轨迹识别强大的车道保持算法
Donghyun Kim1, Yonghwan Jeong2
1Department of Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究介绍了一种机器学习算法,用于在雪地道路上保持稳健的车道. 它准确地检测雪地轮胎的痕迹,可靠地估计车道中心,即使在大雪中.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 车道维护系统在恶劣的天气中扎,例如大雪.
- 现有的算法往往由于车道标志模糊而失败.
研究的目的:
- 开发一个强大的车道保持算法,以应对雪地道路条件.
- 为了准确检测雪地轮胎的痕迹,可靠的车道估计.
主要方法:
- 一个机器学习模型 (YOLOv5) 被训练在雪地轮胎轨迹的定制数据集上.
- 该算法采用了两个模块结构:雪地轮胎轨道检测和车道中心估计.
- 技术包括二元化,鸟视图转换,骨架化,Hough转换和卡尔曼过.
主要成果:
- 雪地轮胎轨迹检测模型在识别雪地道路上的轨迹方面表现出有效性.
- 车道中心估计器成功地利用检测到的轨道来建立可靠的车道参考.
- 该算法提供了强大的车道保持性能,即使在沉重的降雪条件下.
结论:
- 拟议的算法提供了一种可靠的解决方案,用于在具有挑战性的雪环境中保持车道.
- 基于机器学习的轮胎轨迹检测显著提高了在恶劣天气下保持车道系统的性能.
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