PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

IF 2.8 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Physica A: Statistical Mechanics and its Applications Pub Date : 2024-09-26 DOI:10.1016/j.physa.2024.130117
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Abstract

Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscillations through a variety of advanced CAV control strategies, especially those related to the CAV platoon control. Different from the previous works, this paper proposed a platoon-based adaptive cruise control (PACC) strategy for CAV platoon to mitigate the traffic oscillations. The strategy is designed based on the novel and unique leader-following (LF) information topology. Built on the classical proportional-derivative (PD) controller that is implemented in the adaptive cruise control (ACC) strategy of autonomous vehicles (AVs), the PACC strategy is exquisitely designed to ensure the string stability of entire CAV platoon and the critically damped condition of each following CAV in the platoon. Credit to the rapid response of following CAV to the vibration of leading CAV’s dynamic status under LF information topology and the thorough consideration of string stability and damping characteristics in the PD controller design, the PACC strategy enables the CAV platoon to mitigate the traffic oscillations more efficiently than the existing cooperative adaptive cruise control (CACC) and ACC strategies. The numerical experiment for the mixed traffic flow on a single-lane ring road indicates that, when the CAV platoon adopts the PACC strategy, the performance of traffic flow in terms of operational efficiency, driving safety, passenger’s comfort, and fuel economy is substantially enhanced, compared with CACC and ACC strategies. In addition, the performance of PACC strategy gradually improves with the increase of market penetration rate (MPR) of CAVs and length of CAV platoon. Overall, the proposed PACC strategy is a promising solution to the mitigation of traffic oscillation under the CAV environment.
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PACC:基于领队-跟随信息拓扑的排级自适应巡航控制策略,缓解 CAV 环境下的交通振荡
交通振荡通常由车辆跟车行为中的重复加减速动作引起,会对交通流造成许多负面影响。近年来,随着车联网和自动驾驶汽车(CAV)技术的发展,学者们通过各种先进的CAV控制策略,尤其是与CAV排序控制相关的控制策略,在缓解交通振荡传播方面取得了诸多成效。与前人的研究不同,本文提出了一种基于排序的 CAV 排序自适应巡航控制(PACC)策略,以缓解交通振荡。该策略基于新颖独特的领导-跟随(LF)信息拓扑结构设计。PACC 策略基于自动驾驶汽车(AV)自适应巡航控制(ACC)策略中实现的经典比例-派生(PD)控制器,设计精巧,可确保整个 CAV 排的串稳定性以及排中每辆跟随 CAV 的临界阻尼状态。由于在低频信息拓扑结构下,后续 CAV 能对前导 CAV 的动态状态振动做出快速响应,并且在 PD 控制器设计中充分考虑了串稳定性和阻尼特性,因此 PACC 策略能使 CAV 排比现有的协同自适应巡航控制 (CACC) 和 ACC 策略更有效地缓解交通振荡。对单车道环路混合交通流的数值实验表明,当 CAV 排采用 PACC 策略时,与 CACC 和 ACC 策略相比,交通流在运行效率、驾驶安全性、乘客舒适性和燃油经济性等方面的性能均有大幅提升。此外,随着 CAV 市场渗透率(MPR)和 CAV 排长的增加,PACC 策略的性能也会逐渐提高。总之,所提出的 PACC 策略是缓解 CAV 环境下交通振荡的一种有前途的解决方案。
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
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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