深度强化学习用于自动驾驶汽车在非车道交通环境中的决策
Yi Fei1,2, Lu Xing1,2, Lan Yao2
1Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems, Changsha University of Science and Technology, Changsha, China.
PloS one
|April 16, 2025
概括
本研究介绍了一种深度强化学习策略,用于自动驾驶汽车 (AV) 导航复杂的收费站分离区域. 适度的AV采用可以提高交通效率,但过度使用会降低性能.
科学领域:
- 智能运输系统 智能运输系统
- 自动驾驶车辆导航自动驾驶车辆导航
- 交通流动动态 交通流动动态
背景情况:
- 目前的自动驾驶汽车 (AV) 研究主要涉及标准道路,忽视了像没有车道的分离区域这样的复杂场景.
- 收费站分离区对AV决策提出了独特的挑战,这是由于非结构化的环境造成的.
研究的目的:
- 开发和评估基于深度强化学习 (DRL) 的横向运动策略,用于收费站分离区域的AV.
- 分析不同自动驾驶汽车 (AV) 普及率对这些复杂环境中的交通流量和安全的影响.
主要方法:
- 创建了一个微观模拟平台,准确地建模人类驾驶汽车 (HV) 轨迹.
- 采用了DRL算法,具有定制状态和奖励功能,以训练AVs在分离区域中进行高效的横向运动.
- 进行了模拟,以评估单个车辆的性能和不同AV透水平的影响.
主要成果:
- 拟议的DRL策略显著减少了单个车辆的分歧时间.
- 自主效率策略对自动驾驶汽车的适度透提高了整体交通效率和安全.
- 过度的AV透导致道路资源的竞争加剧,对运营条件产生负面影响.
结论:
- 基于DRL的横向运动策略对复杂的收费站分离区域的AV有效.
- 优化AV透对于最大限度地提高交通效率和安全效益至关重要;过度部署可能是有害的.
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