基于深度学习的强大定位,用于全天候自动驾驶
Yasin Almalioglu1, Mehmet Turan2, Niki Trigoni1
1Department of Computer Science, University of Oxford, Oxford, UK.
Nature machine intelligence
|October 4, 2023
概括
这项研究引入了一种深度学习方法,让自动驾驶汽车能够准确地估计自己的位置,即使在恶劣的天气中也是如此. 这种方法将视觉和雷达数据合并为强大的全天候自动驾驶.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自动驾驶汽车 (AV) 在恶劣天气中由于传感器限制而面临精确定位的挑战.
- 目前的AV部署仅限于小规模的试验,阻碍了广泛采用.
- 准确的自我运动估计对于安全可靠的AV操作至关重要.
研究的目的:
- 为自动驾驶汽车开发一个强大的自我运动估计方法.
- 在恶劣天气条件下改善AV本地化能力.
- 创建一个互补的本地化解决方案,克服传感器的缺陷.
主要方法:
- 一种基于深度学习的,自我监督的方法,用于自我运动估计.
- 视觉和雷达传感器数据的几何意识融合使用基于注意力的学习.
- 预测可靠性面具以减轻多式联运数据缺陷.
主要成果:
- 在雨,雾和雪中表现出强大的全天候性能.
- 在各种条件下 (白天/夜晚,恶劣天气) 展示了有效的跨领域通用性.
- 利用游戏理论进行解释,揭示多式联络系统中的独立故障模式.
结论:
- 拟议的方法为在恶劣天气下自动驾驶汽车提供可靠的定位解决方案.
- 这种方法提高了自动驾驶汽车的安全性和可靠性,用于全天候自动驾驶.
- 视觉和雷达数据与可靠性掩盖的融合证明了对强大的导航的有效性.
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