智能汽车决策和轨迹规划方法基于深度强化学习在的空间
Jiawei Wang1, Liang Chu1, Yao Zhang1
1College of Automotive Engineering, Jilin University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了智能汽车的深度强化学习方法,以改善复杂交通中的决策和轨迹规划. 与传统方法相比,这种方法可以提高更改车道的成功率超过13%.
科学领域:
- 智能驾驶系统 智能驾驶系统
- 自动驾驶车辆导航自动驾驶车辆导航
- 深度强化学习学习 (deep reinforcement learning) 是一种深度强化学习的方法.
背景情况:
- 导航复杂的交通环境对智能驾驶技术提出了重大挑战.
- 地图和传感器技术的进步使车辆能够感知其位置和周围的交通动态.
- 现有的方法在复杂的场景中与实时决策和轨迹规划作斗争.
研究的目的:
- 开发一种新的深度强化学习 (DRL) 方法,用于智能汽车决策和轨迹规划.
- 为了应对车道更改和动态交通条件下的机动执行的复杂性.
- 提高自动驾驶系统的安全性和效率.
主要方法:
- 使用DRL框架,集成深度学习,从网格地图表示中提取特征.
- 网格地图包含Frenet坐标系内的静态 (道路标志) 和动态 (车辆位置) 环境数据.
- 动作空间包括车道更改时间,速度和终点位移,通过强化学习进行优化.
主要成果:
- 拟议的DRL方法在CARLA模拟器中在各种驾驶场景中证明了可行性,稳定性和效率.
- 与传统的规划控制算法相比,实现了变轨成功率平均增加6.8%.
- 超越了简单的强化学习方法,显示了变化车道的平均成功率高出13.1%.
结论:
- 开发的DRL方法有效地解决了智能汽车的决策和轨迹规划问题.
- 该方法可显著提高车道更改成功率,增强自动驾驶能力.
- 这项研究为复杂的交通环境中更复杂的自主导航提供了坚实的基础.
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