A review of traffic behavior and intelligent driving at roundabouts based on microscopic perspective

IF 2.7 4区 工程技术 Q2 TRANSPORTATION SCIENCE & TECHNOLOGY
Hao Jiang, Q. Shen, Aoxue Li, Chenhui Yin
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引用次数: 0

Abstract

The contradiction between the increasing traffic and the relatively poor roundabout infrastructure is getting stronger. The control and optimization of the macroscopic traffic flow need to be improved to resolve congestion and safety problems at roundabouts and the connected road network. In order to better understand the gaps and trends in this field, we have systematically reviewed the main research and developments in traffic phenomena, driving behavior, autonomous vehicles (AVs), intelligent connected vehicles, and real vehicle trajectory data sets at roundabouts. The study is based on 388 papers about roundabouts, selected through a comprehensive literature search. The review demonstrates that based on a microscopic perspective, sensing, prediction, decision-making, planning, and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at roundabouts. However, the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex, which is a tremendous challenge for the systematic design of AVs. Therefore, based on naturalistic driving data and machine learning theory, it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviors.
基于微观视角的环形交叉口交通行为与智能驾驶研究综述
日益增长的交通量和相对较差的环形交叉口基础设施之间的矛盾越来越强烈。需要改善宏观交通流的控制和优化,以解决环形交叉口和相连道路网的拥堵和安全问题。为了更好地了解该领域的差距和趋势,我们系统地回顾了交通现象、驾驶行为、自动驾驶汽车(AV)、智能网联汽车和环形交叉口真实车辆轨迹数据集的主要研究和发展。这项研究基于388篇关于环形交叉路口的论文,这些论文是通过全面的文献检索选出的。该综述表明,基于微观视角,可以设计和优化电动汽车和智能网联汽车的传感、预测、决策、规划和控制方面,从根本上显著提高环形交叉口的通行能力和驾驶安全。然而,环形交叉口交通参与者之间交通冲突的产生机制是复杂的,这对AVs的系统设计是一个巨大的挑战。因此,基于自然驾驶数据和机器学习理论,通过学习和模仿人类驾驶员的决策和驾驶行为来建立驾驶员模型是一个重要的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Transportation Safety and Environment
Transportation Safety and Environment TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.90
自引率
13.60%
发文量
32
审稿时长
10 weeks
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