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关于在狭窄空间中自动驾驶汽车轨迹规划的研究
Yunlong Li1, Gang Li1, Xizheng Wang1
1School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究使用改进的混合A星算法在复杂环境中增强了自动驾驶汽车轨迹规划. 优化的方法提高了效率,并确保了车辆流舒适的移动.
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:
- 自动驾驶汽车需要高效的轨道规划,以确保安全航行.
- 复杂和受限制的环境对现有的规划算法构成重大挑战.
- 目前的方法可能缺乏在现实世界中部署所需的适应性和流性.
研究的目的:
- 增强混合A星算法,以改善复杂环境中的轨迹规划.
- 开发一种提高规划效率并确保车辆平稳运动的方法.
- 在具有挑战性的场景中解决当前轨道规划技术的局限性.
主要方法:
- 引入了一个自适应节点扩展策略来管理环境复杂性.
- 整合了Dijkstra的最短路径搜索,以改进成本估计和方向选择.
- 使用自定义约束的二次编程来平滑离散路径点.
- 在平滑的轨迹上使用S曲线配置文件进行速度规划.
主要成果:
- 改进的混合A星算法在模拟和实验中显著提高了规划效率.
- 由此产生的轨迹在方向角和速度上呈现出连续而平稳的过渡.
- 该方法有效处理复杂和受限制的环境.
- 自动驾驶汽车的整体舒适性得到了显著改善.
结论:
- 拟议的轨迹规划方法比标准混合动力A-star.提供了显著的改进.
- 自适应扩展,迪克斯特拉的搜索和二次编程的整合产生了高效和流的轨迹.
- 这种方法提高了自动驾驶汽车在复杂的现实驾驶条件下的可行性.
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