通过深度神经网络优化通过适应性进化战略的多车道差异可变速度限制控制
Jianshuai Feng1, Tianyu Shi2, Yuankai Wu3
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种使用共变矩阵适应进化策略 (CMA-ES) 进行变速限制控制的新方法. 该方法通过动态优化跨车道的速度限制来改善交通流量并减少排放.
科学领域:
- 智能运输系统 智能运输系统
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 变速限制对于先进的交通管理至关重要.
- 深度强化学习 (DRL) 面临着延迟奖励和交通控制趋同的挑战.
- 进化策略为复杂的控制问题提供了强大的优化.
研究的目的:
- 开发一种新的多车道差速变速限制控制方法.
- 解决传统的DRL在交通管理中的局限性,特别是延迟奖励和融合不稳定性.
- 通过智能控制策略来提高高速公路的吞吐量和减少排放.
主要方法:
- 使用共变矩阵适应演化策略 (CMA-ES),一种无梯度优化方法.
- 采用基于深度学习的模型来进行动态的,特定车道的速度限制学习.
- 通过多变量正常分布优化神经网络参数,使用基于高速公路吞吐量的动态更新协差矩阵.
主要成果:
- 拟议的CMA-ES方法在模拟中显著超过了DRL,传统的进化方法和无控制场景.
- 实现了平均旅行时间的23%改善.
- 平均减少了4%的CO,HC和NOx排放,速度限制和强烈的概括性是可以解释的.
结论:
- 基于CMA-ES的方法在复杂的交通场景中有效地管理可变速度限制.
- 这种方法为交通控制提供了一个强大的,高效的替代传统的DRL,提高效率和环境影响.
- 该方法提供了可解释的控制行动,并且可以很好地对不同的交通条件进行概括.
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