研究基于GCN和多段多项式曲线优化自动驾驶汽车的车道更改决策和规划
Fuyong Feng1,2, Chao Wei1,3, Botong Zhao1
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种使用图形卷积网络 (GCN) 和多项式曲线的新自动驾驶汽车变轨方法,以实现更安全,更高效的驾驶. 该方法在复杂的交通场景中增强了决策和轨道规划.
科学领域:
- 自主系统 自主系统
- 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:
- 导航动态的驾驶环境需要复杂的决策和自动驾驶汽车的轨迹规划.
- 了解和建模车辆之间的互动效应对于安全有效的车道更改至关重要.
研究的目的:
- 为自动驾驶车辆改变车道的行为决策和轨迹规划提出先进的方法.
- 为了提高性能,利用图形卷积网络 (GCN) 和多段多项式曲线优化.
主要方法:
- 使用图形结构数据对动态驾驶环境进行分层建模.
- 图形卷积神经网络 (GCNs) 的应用,用于处理交互信息和生成行为命令.
- 基于优化的多段多项式曲线轨迹规划,以确保无碰撞的运动.
主要成果:
- 与传统方法相比,拟议的方法在模拟和现实世界的实验中表现出更高的性能.
- 实现了良好的稳定性,实时性能和强大的场景概括能力.
- 成功生成了无碰撞,动态受约束的运动轨迹,用于改变车道的机动.
结论:
- GCNs和多项式曲线优化方法为自动驾驶汽车改变车道提供了强大而高效的解决方案.
- 该方法有效地处理动态驾驶环境中的复杂相互作用.
- 通过全面的模拟和道路车辆实验验证实有效性.
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