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用预测信息和人工潜力场来规划自动驾驶汽车避免碰撞的路径
Sumin Ahn1, Taeyoung Oh1, Jinwoo Yoo2
1Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究通过将周围车辆预测数据整合到人工潜力场 (APF) 和优化路径规划来提高自动驾驶的安全性. 这种方法克服了APF.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 机器人和控制理论
- 路径规划算法 路径规划算法
背景情况:
- 自动驾驶技术的进步需要强大的应急规避路径规划.
- 当前的人工潜力场 (APF) 方法面临局部最小问题,阻碍了路径完成.
- 整合周围车辆的预测对于提高驾驶安全和稳定性至关重要.
研究的目的:
- 为自动驾驶汽车开发一个改进的应急规避路径规划方法.
- 为解决传统APF算法固有的局部最小问题.
- 通过预测性路径规划,提高自动驾驶的效率和稳定性.
主要方法:
- 将周围车辆预测数据集成到人工潜力场 (APF) 框架中.
- 使用顺序二次编程优化五度贝齐尔曲线控制点.
- 使用IPG CarMaker和MATLAB/Simulink环境进行模拟验证.
主要成果:
- 拟议的方法有效地将预测数据集成到APF中,以改进路径规划.
- 优化贝齐尔曲线可以提高轨迹生成和稳定性.
- 模拟证实了该方法在复杂场景中的有效性和有效性.
结论:
- 这种新的方法成功地减轻了APF路径规划中的局部最小问题.
- 整合预测数据和优化贝齐尔曲线,可以实现更高效,更稳定的自动驾驶.
- 经过验证的方法为自动驾驶汽车的安全系统提供了重大进步.
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