基于注意力机制的自动驾驶汽车的障碍回避优化和路径规划研究与机器人的多式联络信息决策思想相结合
Xuejin Wu1, Guangming Wang2,3, Nachuan Shen4
1College of Transport and Communications, Shanghai Maritime University, Shanghai, China.
Frontiers in neurorobotics
|October 9, 2023
概括
本研究介绍了端到端架构中的注意力机制,以增强自动驾驶. 该方法通过智能处理多式联运传感器数据以实现更安全的导航,优化了障碍回避和路径规划.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉和传感器融合
- 运输工程 运输工程
背景情况:
- 自动驾驶技术在很大程度上依赖于先进的机器感知和多式联运决策,用于导航,路径规划和避开障碍物.
- 当前的系统在准确解释复杂环境和做出实时,明智的决策方面面临着挑战.
- 优化这些功能对于自动驾驶汽车的进步和广泛采用至关重要.
研究的目的:
- 研究在端到端架构中注意力机制的有效性,以改进自动驾驶汽车中避免障碍物和路径规划.
- 通过使车辆能够专注于来自多式联运数据的关键信息来增强环境感知和决策.
- 利用经典机器人原理和长短期记忆 (LSTM) 模型进行动态导航和路线优化.
主要方法:
- 开发了一个结合注意力机制的端到端架构来处理多式联络输入 (图像,LiDAR).
- 用注意力机制来动态权衡关键的环境特征,以避免障碍.
- 长期短期记忆 (LSTM) 模型被用来将导航转化为一个连续的决策问题,优化路径规划,以注意力加权的关键点.
主要成果:
- 拟议的方法证明了在各种道路场景中有效避免障碍物和遵守交通规则.
- 在真实道路数据集上的实验评估证实了稳定,安全和高效的自动驾驶性能.
- 该系统通过根据实时条件动态调整路线,成功优化了路线规划.
结论:
- 集成到端到端架构中的注意力机制为优化在自动驾驶中避开障碍物和路径规划提供了强大的解决方案.
- 该方法增强了多式联运信息融合,有助于改进导航,本地化和人机交互.
- 这项研究推动了智能系统的实际应用,以实现更安全,更有效的自主运输.
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