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风险规则过的LSTM对决DQN,用于自主改变车道的决定
1School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China.
Traffic injury prevention
|January 20, 2026
概括
一个新的风险规则过的长期短期记忆决斗深度Q网络 (LSTM-DDQN) 增强了自动驾驶车辆的车道更改决策. 该方法通过将风险评估与深度强化学习相结合,提高复杂交通中的安全性和效率.
科学领域:
- 智能运输系统 智能运输系统
- 自动驾驶自动驾驶的自动驾驶
- 机器学习 机器学习
背景情况:
- 自动驾驶汽车改变车道的决策对于智能交通系统至关重要.
- 在动态交通中确保安全和效率是一个重大挑战.
研究的目的:
- 在复杂的交通场景中提出一种先进的方法,用于自主改变车道的决策.
- 通过一种新的深度强化学习方法,提高车道更改的安全性和效率.
主要方法:
- 一个经过风险规则过的长期短期记忆决斗深度Q网络 (LSTM-DDQN) 被开发出来.
- 基于时间到碰撞 (TTC) 的过器识别高风险车辆.
- LSTM处理时间流量数据,将其输入到决斗DQN以进行动作选择.
主要成果:
- 与标准的决斗DQN基线相比,LSTM-DDQN模型显示正常化平均单步奖励增加了10.9%.
- 在相同的模拟设置下评估性能,隔离了拟议模块的影响.
结论:
- 拟议的LSTM-DDQN方法显著改善了自动驾驶汽车的车道更改决策.
- 城市移动模拟 (SUMO) 平台的验证证实了其在提高车道更换安全性方面的卓越表现.
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