一个用于优化基于深度学习的车道检测和自动驾驶方向盘的框架
Daniel Yordanov1, Ashim Chakraborty1, Md Mahmudul Hasan1
1School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究开发了一种新的框架,用于自动驾驶汽车检测车道并准确地驾驶. 该系统在轻松条件下实现了77%的自主性,在具有挑战性的道路上达到66%.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 准确的车道检测和跟踪对于自动驾驶汽车的安全性和可靠性至关重要.
- 现有系统在多样化和复杂的驾驶环境中面临挑战.
研究的目的:
- 为优化自动驾驶汽车车道检测和方向盘控制提供一个新的框架.
- 开发一个强大的系统,能够在各种道路场景中运行.
主要方法:
- 在Unity3D中创建了一个虚拟的沙箱环境,用于程序路和驱动生成.
- 一个卷积神经网络 (CNN) 在生成的数据集上进行了训练,用于车道检测和自主转向.
- 该模型使用来自Comma.ai.ai的真实世界驾驶录像进行了评估.
主要成果:
- 训练有素的行为驾驶模型展示了有效的车道检测和自主转向能力.
- 该系统在低挑战的道路条件下实现了77%的自主性.
- 自主性降低到66%的道路与尖的转,表明需要进一步改进的领域.
结论:
- 拟议的框架显示了增强自动驾驶车辆在车道后任务中的导航的希望.
- 需要进一步的研究,以提高在更复杂和动态的驾驶情况下的性能.
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