在混合交通网络中制定生态驾驶策略,使用强化学习.
Umar Jamil1, Mostafa Malmir1, Alan Chen2
1Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA.
Science progress
|July 23, 2024
概括
使用自动驾驶汽车 (AV) 和交通信号灯协调的环保驾驶策略显著降低了燃料消耗和车辆延误. 10%的AV透率优化了混合动力交通网络中的生态驾驶.
科学领域:
- 交通工程是交通工程.
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 生态驾驶对于减少温室气体排放和改善公共卫生至关重要.
- 将自动驾驶汽车 (AV) 整合到交通网络中,在数据管理和交通控制方面带来了挑战.
- 现有的交通网络很复杂,这阻碍了对生态驾驶策略的准确数学建模.
研究的目的:
- 开发和评估以自动驾驶和人驾驶车辆 (HDV) 为基础的混合交通网络的生态驾驶战略.
- 为交通控制人员解决实时数据共享和决策方面的挑战.
- 通过协调AV和交通信号灯控制,优化燃油效率并减少车辆延误.
主要方法:
- 使用城市移动模拟 (SUMO) 模拟器对交通数据进行计算分析.
- 采用无模型的强化学习 (RL) 算法,近接政策优化,用于决策.
- 在交通流中引入不同百分比的AV (5%,10%,20%),以评估影响.
主要成果:
- 10%的AV透率显示了最快的趋同到最佳的平均奖励.
- 这一战略导致整个交通网络的燃料消耗大幅减少.
- 观察到所有车辆 (自动驾驶汽车和重型车) 的总延迟显著减少.
结论:
- 混合交通网络具有10%的AV透率和RL协调的交通信号灯提供了一个有效的生态驾驶策略.
- 这种方法提高了燃料效率,并最大限度地减少了整体交通延误.
- 该研究强调了人工智能在优化可持续城市流动方面的潜力.
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