计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (12): 1-17.DOI: 10.3778/j.issn.1002-8331.2310-0310

• 热点与综述 • 上一篇    下一篇

合流区智能网联汽车协同控制方法综述

李春,吴志周,曾广,赵鑫,杨志丹   

  1. 1.新疆大学 智能制造现代产业学院,乌鲁木齐 830017
    2.新疆大学 交通运输工程学院,乌鲁木齐 830017
    3.同济大学 交通运输工程学院,上海 201804
  • 出版日期:2024-06-15 发布日期:2024-06-14

Review of Connected Autonomous Vehicle Cooperative Control at On-Ramp Merging Areas

LI Chun, WU Zhizhou, ZENG Guang, ZHAO Xin, YANG Zhidan   

  1. 1.School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China
    2.School of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China
    3.College of Transportation Engineering, Tongji University, Shanghai 201804, China
  • Online:2024-06-15 Published:2024-06-14

摘要: 车辆进行交会的区域被指定为上匝道合流区。如果主线和匝道交通流密度达到饱和,匝道合流区的交通效率就会急剧下降。智能网联技术作为当前的交通上的研究热点,依靠智能网联汽车(connected-automated vehicle, CAV)的高精度运动控制和高效率通信,可以显著地提高合流区的通行效率。针对三种不同的控制范式:反馈控制、最优控制和强化学习,对CAV使用的融合策略进行了评估。通过对现有研究的回顾,总结了三种方法在这种情况下的不足之处,并给出了具体的改进措施。此外,全面地总结了这一特定科学领域的最新发展和趋势。

关键词: 匝道合流区, 互联和自动驾驶车辆, 强化学习, 优化控制

Abstract: The area where vehicles conduct interchanges is designated as the on-ramp merging area. The traffic efficiency in the ramp merging area drastically decreases if the mainline and ramp traffic flow density reaches saturation. As a current research hotspot in transportation, intelligent network technology, relying on the high-precision motion control and high-efficiency communication of connected-automated vehicle (CAV), can significantly improve the traffic efficiency in the merging area. The fusion strategies used by CAV are assessed in this research utilizing three different control paradigms: feedback control, optimal control, and reinforcement learning. The shortcomings of the three methods in this scenario are summarized, and specific improvement measures are given by reviewing existing research. Also, it offers a thorough summary of the most recent developments and trends in this particular scientific field.

Key words: on-ramp merging areas, connected and autonomous vehicles, reinforcement learning, optimal control