在V2X通信中提高最小的意识失败距离:一种深度强化学习方法
Anthony Kyung Guzmán Leguel1, Hoa-Hung Nguyen1, David Gómez Gutiérrez2,3
1Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究引入了V2X通信中车辆意识的新定义. 深度强化学习框架 (DRL-JCBRTP) 通过优化信标策略来提高驾驶安全.
科学领域:
- 车辆通信网络 车辆通信网络
- 智能运输系统 智能运输系统
- 机器学习用于自动驾驶.
背景情况:
- 合作意识对于车辆网络至关重要,通常由车辆的感知和共享动态数据来定义.
- 现有的意识指标往往忽视了车辆检测,跟踪和维护安全距离的复杂性.
研究的目的:
- 提出一种新的,多方面的意识定义,用于车辆到一切 (V2X) 通信.
- 引入深度强化学习框架,共同控制信标速率和发射功率 (DRL-JCBRTP),以提高V2X的认识.
- 通过最小化意识失败概率和最大化意识距离来提高驾驶安全.
主要方法:
- 开发了一个深度强化学习框架 (DRL-JCBRTP),在软演员-批评 (SAC) 算法中使用基于LSTM的演员和基于MLP的批评网络.
- 实施了创新的奖励功能,以增加最低意识失败距离,利用地方状态信息.
- 使用SLMLab-Gym-VEINS进行模拟,以评估拟议框架的性能.
主要成果:
- 在模拟中,DRL-JCBRTP方案显著优于现有的信标方案.
- 证明了意识失败概率的降低.
- 显示了最大可实现的意识距离的增加.
结论:
- 拟议的DRL-JCBRTP框架为提高V2X网络中的合作意识提供了一种优越的方法.
- 通过深度强化学习优化信标速率和传输功率,可以提高驾驶安全.
- 新的定义和框架推动了智能交通系统领域的发展.
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