对自动驾驶汽车的决策和规划方法的调查
Jun Hu1,2, Yuefeng Wang2, Shuai Cheng2
1School of Software, Shenyang University of Technology, Shenyang, China.
Frontiers in neurorobotics
|March 5, 2025
概括
本文审查了自动驾驶系统的决策和规划算法. 它比较了以知识驱动,数据驱动和混合方法来推进自动驾驶汽车技术.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 运输工程 运输工程
背景情况:
- 自动驾驶技术有望通过先进的机器人系统改变交通运输.
- 复杂的自动驾驶系统仍处于实验阶段,决策和规划算法是其成功的关键.
研究的目的:
- 为自动驾驶提供决策和规划算法的全面审查.
- 分析知识驱动,数据驱动和混合方法,包括它们的优点,缺点和实验平台.
主要方法:
- 对知识驱动方法的审查:基于规则,基于状态过渡,基于游戏理论的决策;基于搜索,基于采样,基于优化的规划.
- 数据驱动方法的分析:模仿学习,强化学习,反强化学习.
- 讨论混合模型,结合知识驱动和数据驱动的方法.
主要成果:
- 对各种算法的优缺点进行比较分析.
- 评估用于测试和验证策略的实验平台.
- 了解不同自动驾驶算法的实施和挑战.
结论:
- 通过阐明方法比较,促进更强大的自动驾驶系统的设计.
- 解决当前的挑战,并为自动驾驶技术发展提供未来的视角.
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