基于CAS-UNet和图形神经网络的无人自动驾驶汽车的动态路径规划
Yuchu Ji1, Rentong Sun1, Yang Wang1
1College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
在动态环境中,CAS-GNN模型增强了多代理车辆路径规划. 它通过适应性处理信息和识别障碍物,实现了92.8%的成功率和提高效率.
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
- 计算机科学 计算机科学
背景情况:
- 多代理车辆系统需要复杂的路径规划算法,以确保安全高效的运行.
- 动态环境由于不可预测的变化和移动障碍而带来重大挑战.
研究的目的:
- 开发一个综合模型,CAS-GNN,用于在动态多代理车辆场景中的协作路径规划.
- 加强障碍物边界识别和调整信息传输以提高规划准确性.
主要方法:
- 将CAS-UNet和图形神经网络 (GNN) 集成到CAS-GNN模型中.
- 引入动态边缘增强和重量更新模块,用于适应性信息处理.
- 利用在线轨迹优化进行数据生成,以提高适应性.
主要成果:
- 在动态路径规划模拟中,CAS-GNN表现出强的性能.
- 实现了92.8%的成功率,0.0836%的碰撞率,和64%的轨迹效率与六辆车.
- 超过了传统的A-GNN模型,成功率增加了2.7%,效率增加了8%.
结论:
- 在复杂的,动态的环境中,CAS-GNN模型为协作路径规划提供了一个强大的解决方案.
- 拟议的模型显著提高了规划成功率和轨道效率,同时减少了碰撞.
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