一种基于深度强化学习的自动驾驶汽车行为决策方法,使用混合状态空间和驱动风险的深度强化学习
Xu Wang1, Bo Qian1, Junchao Zhuo1
1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了用于自动驾驶汽车行为决策的深度强化学习 (DRL) 方法,重点是尽量减少驾驶风险. 新方法在复杂的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
- 运输工程 运输工程
背景情况:
- 行为决策对于智能汽车和智能交通系统至关重要.
- 高效和安全的决策是自动驾驶的关键研究领域.
研究的目的:
- 为自动驾驶汽车行为决策提出深度强化学习 (DRL) 方法.
- 为了使自动驾驶汽车能够在最小的瞬间驾驶风险下做出决定.
主要方法:
- 一种在高速行驶时自动驾驶汽车驾驶风险计算方法.
- 深度强化学习 (DRL) 通过车辆与环境的相互作用来提高决策的安全性和效率.
- 在各种模拟场景中进行培训和验证.
主要成果:
- 拟议的DRL方法可以在复杂的驾驶环境中进行安全和高效的行为决策.
- 与先进的算法相比,在驾驶距离 (3.3%),安全 (2.1%) 和计算时间 (43%) 中取得了明显的改进.
结论:
- 开发的DRL方法有效地解决了自动驾驶汽车行为决策方面的挑战.
- 这种方法显著提高了自动驾驶系统的性能和安全性.
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