基于学习的等级决策框架,用于在不完全连接的交通场景中自动驾驶
Fan Yang1, Xueyuan Li1, Qi Liu1
1School of Mechanical Engineering, Beijing Institute of Technology, Zhongguancun South Street, Beijing 100081, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一个稳定的等级决策框架,用于使用图像输入的自动驾驶. 它有效地处理现实世界的驾驶场景,而不需要全球网络信息.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 端到端的决策算法提供强大的数据处理,但存在不确定性.
- 现有的基于学习的方法需要理想状态信息,这限制了它们在现实世界中自动驾驶的适用性.
- 在复杂环境中自动驾驶需要在不完整的信息下做出强有力的决策.
研究的目的:
- 为自动驾驶系统提出一个稳定的等级决策框架.
- 解决现有算法在处理现实世界,不完整信息方面的局限性.
- 为了实现可靠的自动驾驶,仅使用图像输入.
主要方法:
- 一个基于模型的数据编码器将输入图像转换为通用数据格式.
- 使用时间序列图形卷积网络 (GCN) 的状态机分类驱动状态.
- 基于规则的算法被选择用于基于分类的驾驶状态的动作生成.
主要成果:
- 拟议的框架可以在各种交通场景中成功执行自动驾驶任务.
- 该系统在不需要全球网络信息的情况下有效运行.
- 比较实验验证了层次框架,图像数据编码器和时间序列GCN的有效性.
结论:
- 层次决策框架为自动驾驶提供了稳定有效的解决方案.
- 基于模型的图像编码器和时间序列GCN是强大的状态分类和决策的关键组成部分.
- 这种方法提高了自动驾驶汽车在现实环境中的可靠性.
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