一种基于机器学习的方法来评估自动驾驶汽车对路面粗度的影响
Chenxi Chen1, Yang Song1, Yizhuang David Wang2
1Department of Civil and Environmental Engineering, Pennsylvania State University, Sackett Building, University Park, PA 16802, USA.
概括
连接和自动化车辆 (CAV) 导致人行道恶化速度比人驾驶车辆 (HDV) 快8.1%. 这种影响在较年轻的道路上更大,并且随着交通拥堵而增加.
科学领域:
- 运输工程 运输工程
- 铺路工程工程 铺路工程
- 基础设施中的人工智能
背景情况:
- 现有关于互联和自动化汽车 (CAV) 的研究主要研究它们的流浪模式.
- 在了解CAV与人驾驶车辆 (HDV) 对交通基础设施,特别是路面性能的比较影响方面存在差距.
- 这项研究解决了对CAV和HDV驾驶行为及其基础设施影响进行果对果比较的需求.
研究的目的:
- 系统地比较CAV和HV的轨迹模式.
- 根据驾驶模式和上下文变量,开发一款路面性能预测模型.
- 量化CAV和HV对路面恶化的差异影响.
主要方法:
- 利用来自下一代模拟 (NGSIM) 数据集和共同模拟平台 (CARLA和SUMO) 的数据来获取车辆轨迹数据.
- 开发了一个基于梯度增强的集体学习模型来预测路面性能 (国际粗度指数).
- 输入特征包括驾驶模式指标 (侧向偏差,后距离,速度) 和20个上下文变量,这些变量是根据长期路面性能数据库的1707个观察结果进行训练的.
主要成果:
- 开发的模型准确地预测了路面的恶化.
- 发现CAV的平均损坏路面比HV多8.1%.
- 灵敏度分析显示,CAV对年轻人行道的影响更大,并且在交通拥堵的情况下,恶化率会增加.
结论:
- 与HV相比,CAV加速了路面的恶化.
- 路面的磨损被CAV加剧,特别是在新的基础设施和繁忙的交通中.
- 研究结果强调,需要在路面管理和设计策略中考虑CAV特有的影响.
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