在复杂的道路上使用自动驾驶汽车的卷积注意力机制进行有效的车道检测
Vinay Maddiralla1, Sumathy Subramanian2
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|August 19, 2024
概括
这项研究引入了一个新的车道检测与卷积注意力机制 (LD-CAM) 模型用于自动驾驶汽车. LD-CAM模型在极端天气和糟糕的道路基础设施等具有挑战性的条件下显著提高了车道检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶汽车 (AV) 需要强大的感知系统来安全操作.
- 现有的车道检测模型在极端天气和道路质量差等不利条件下扎.
- 准确的车道检测对于AV的车道保持和离开警告至关重要.
研究的目的:
- 为在具有挑战性的环境中运行的自动驾驶汽车开发准确的车道检测方法.
- 解决当前深度学习模型在不良,曲线或未标记的道路以及极端天气中检测车道方面的局限性.
- 提出一种新型模型,即带有卷积注意力机制 (LD-CAM) 的车道检测,用于增强车道检测.
主要方法:
- 拟议的LD-CAM模型使用编码器-解码器架构.
- 集成了一个增强的卷积块注意模块 (E-CBAM),以改进特征地图质量.
- 该模型在不同的数据集上进行了训练和验证,包括Tusimple,Curve Lanes以及Cracks and Potholes.
主要成果:
- LD-CAM模型实现了高性能指标:97.90%的准确性,98.92%的精度,97.90%的F1-Score,98.50%的IOU和98.80%的子系数.
- 在极端天气条件下,在结构化和有缺陷的道路上都表现出卓越的性能.
- 在具有挑战性的场景中超越现有模型的表现,目前文献没有很好地处理.
结论:
- LD-CAM模型为自动驾驶汽车的车道检测提供了显著的进步.
- 它甚至在恶劣的环境和道路条件下提供可靠的车道检测功能.
- 这项研究有助于开发更安全,更可靠的自动驾驶系统.
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