对自动驾驶汽车局部路径规划的基于利亚普诺夫优化方法
Ziba Arjmandzadeh1, Mohammad Hossein Abbasi2, Hanchen Wang1
1School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
研究人员开发了一种新的利亚普诺夫优化 (LO) 方法,用于自动驾驶汽车 (AV) 路径规划. 与传统方法相比,这种仅视觉的方法显著减少了超过20倍的计算时间.
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
- 控制系统 控制系统
背景情况:
- 自动驾驶汽车 (AV) 承诺提高安全性和效率.
- 实现完全自主 (SAE 5 级) 取决于有效的路径规划.
- 目前的路径规划方法在计算复杂性和安全性方面面临挑战.
研究的目的:
- 引入一种新的Lyapunov优化 (LO) 方法,用于AVs的本地路径规划.
- 为了评估LO方法的性能与传统技术相比.
- 评估用于AV路径规划的仅视觉系统的可行性.
主要方法:
- 为AV局部路径规划开发了一种新的利亚普诺夫优化 (LO) 模型.
- 该LO模型与模型预测控制和基于抽样的方法进行了基准测试.
- 在俄克拉荷马州诺曼,一个AV原型利用仅视觉系统进行对象检测和数据收集.
主要成果:
- 与基线方法相比,拟议的LO策略至少减少了计算时间的20倍.
- 基于路径平滑性,安全性和计算时间来评估性能.
- 只有视觉的方法在现实世界的适用性和成本降低方面被证明是有效的.
结论:
- 利亚普诺夫优化为AV局部路径规划提供了一个高效的解决方案.
- 只有视觉实现证明了自动驾驶系统的实际可行性.
- 这项研究有助于提高自动驾驶汽车的安全性和计算效率.
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