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。
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.
作者
李春
吴志周
曾广
赵鑫
杨志丹
LI Chun;WU Zhizhou;ZENG Guang;ZHAO Xin;YANG Zhidan(School of Intelligent Manufacturing Modern Industry,Xinjiang University,Urumqi 830017,China;School of Traffic and Transportation Engineering,Xinjiang University,Urumqi 830017,China;College of Transportation Engineering,Tongji University,Shanghai 201804,China)
出处
《计算机工程与应用》
CSCD
北大核心
2024年第12期1-17,共17页
Computer Engineering and Applications
基金
国家自然科学基金(52172330)。
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
on-ramp merging areas
connected and autonomous vehicles
reinforcement learning
optimal control
