自主监督的方向盘和自动驾驶的路径标签
Andrei Mihalea1, Robert-Florian Samoilescu1, Adina Magda Florea1
1Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种新的自主驾驶自主驾驶方法,仅使用单眼相机数据. 它从未标记的镜头中生成方向和路径标签,使得可靠的政策学习成为更安全的城市导航.
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:
- 当前的自动驾驶解决方案在城市环境中与准确性和安全性作斗争.
- 现有的方法通常需要昂贵的硬件,并且容易受到数据分布转移的影响,这阻碍了实际采用.
研究的目的:
- 通过使用易于获得的数据,开发一个具有成本效益和强大的自动驾驶方法.
- 从大型,多样化的在线数据集中学习复杂的驾驶政策.
主要方法:
- 一种自我监督的方法,只使用单眼相机输入.
- 从未标记的数据生成方向盘数据注释的新机制.
- 一个单独的管道,以完全自我监督的方式生成路径标签.
主要成果:
- 拟议的方法可以在没有监督的情况下生成有价值的方向和路径标签.
- 它利用未标记的单眼相机数据进行数据生成.
- 这种方法有助于学习强大的自动驾驶政策.
结论:
- 这种方法为利用大量的在线数据为自动驾驶提供了一个实际的步骤.
- 它通过减少硬件依赖和提高稳定性来解决当前方法的局限性.
- 自主监督的驾驶数据生成是可扩展和安全的自动驾驶系统的关键.
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