一个任务级的紧急经验重复使用方法,用于高速公路事故的现场处置与政策提炼的强化学习
Longhao Yan1, Ping Wang2, Fan Qi3
1Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore.
Accident; analysis and prevention
|June 29, 2023
概括
这项研究引入了一种新的方法,用于重用过去的高速公路事故数据,以改善应急响应. 政策提炼的多代理深度决定性政策梯度 (PD-MADDPG) 算法提高了决策的速度和有效性.
科学领域:
- 人工智能的人工智能
- 交通安全 交通安全 交通安全
- 应急管理 应急管理
背景情况:
- 高速公路事故数据丰富,但未被充分利用,以改善应急响应.
- 现有的方法很难有效地从过去的事故处置经验中转移知识.
- 需要更快,更优的现场事故管理是至关重要的.
研究的目的:
- 提出一种基于知识的经验转移方法,用于高速公路事故处理.
- 通过重新使用过去的事故数据来加强紧急决策.
- 开发一种适应性算法,以实现最佳的现场处置.
主要方法:
- 利用马尔科夫决策过程来模拟多种类型的高速公路事故场景.
- 开发了经验转移的政策提炼多代理深度决定性政策梯度 (PD-MADDPG) 算法.
- 在中国山西省的现实高速公路事故案例中评估算法.
主要成果:
- 与传统的决策方法相比,PD-MADDPG算法显示出更高的性能.
- 使用转移知识的决策者在五起事故案例中获得了明显更高的平均回报 (高达65.22%).
- 该方法促进了更快的紧急决策和更有效的现场事故处理.
结论:
- 建议的经验转移方法有效地利用了过去的高速公路事故数据.
- PD-MADDPG算法为改善应急响应系统提供了一个有希望的方法.
- 转移的知识显著提高了事故管理的速度和最佳性.
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