在城市场景中增强自动驾驶:一种混合方法,包括强化学习和经典控制
Rodrigo Gutiérrez-Moreno1, Rafael Barea1, Elena López-Guillén1
1Electronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, Spain.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了自动驾驶汽车的混合决策系统,将深度强化学习与经典方法相结合. 这种新的方法在现实驾驶场景中提高了安全性和效率.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
背景情况:
- 自动驾驶汽车的决策系统经常与现实世界的复杂性作斗争.
- 深度增强学习 (DRL) 是有前途的,但在关键应用中缺乏可靠性.
- 古典方法提供了可靠性,但缺乏适应性.
研究的目的:
- 开发和验证用于自动驾驶的混合决策模块.
- 将DRL的学习能力与传统控制方法的稳定性相结合.
- 使用高清地图和传感器数据生成可靠的方向盘和速度信号.
主要方法:
- 制定了决策问题作为一个部分可观察的马尔科夫决策过程 (POMDP).
- 实现了混合架构,将DRL算法与经典控制模块结合起来.
- 在CARLA模拟器中使用连接的驾驶场景验证了系统.
主要成果:
- 拟议的混合系统成功地产生了方向盘和速度命令.
- 与现有方法相比,该架构展示了增强的安全性和舒适性.
- 在场景完成时间上表现优于CARLA自动驾驶仪.
结论:
- 混合决策系统有效地将DRL和自动驾驶的经典方法合并在一起.
- 经过验证的系统为自动驾驶汽车导航提供了现实的,高效的解决方案.
- 这种方法在模拟环境中改进了现有的自动驾驶堆.
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