转向角度预测和控制器设计基于改进的YOLOv5用于电线式转向系统
Cunliang Ye1,2, Yunlong Wang1, Yongfu Wang1
1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了YOLOv5Ms,这是一款用于准确预测自动驾驶汽车转向角度的轻型模型,提高了效率和控制. 与传统方法相比,新方法显著减少了错误.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 控制系统工程 控制系统工程
- 计算机视觉 计算机视觉
背景情况:
- 转向角度预测对于自动驾驶汽车 (AV) 控制至关重要,但像YOLOv5这样的传统方法在准确性和效率方面存在局限性.
- 控制转向角度是具有挑战性的,原因是测量角度速度的困难和外部因素,如干扰和摩擦.
研究的目的:
- 提出一个轻量级的转向角度预测网络,YOLOv5Ms,提高AV的计算效率和预测准确性.
- 开发一个具有输出约束的自适应输出反控制方案,以有效调节预测的转向角度.
- 解决现有的车道线数据集缺乏多样性的问题,通过从现实世界的道路图像创建一个新的数据集.
主要方法:
- 开发了YOLOv5Ms,这是一款基于YOLOv5的轻量级模型,结合了Shape-IoU_Loss以改进边界框回归.
- 从真实道路创建了一个新的车道数据集来训练网络模型.
- 设计了一个自适应神经网络输出反控制器,使用后退和利亚普诺夫屏障功能进行受约束的输出控制.
主要成果:
- 与YOLOv5s相比,YOLOv5Ms实现了30.34%的重量储存减少和7.38%的精度增加.
- 拟议的控制方案显示,根平均平方误差 (RMSE) 比传统的后退控制优于39.16%.
- 该系统有效地估计了角度,角度速度和未知的干扰,确保了闭环系统的稳定性.
结论:
- YOLOv5Ms模型为自动驾驶汽车的转向角度预测提供了显著的效率和准确性的改进.
- 适应输出反控制方案有效调节转向角度,在精度和稳定性方面超过传统方法.
- 综合方法确保了自动驾驶系统的稳定可靠控制.
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