Planning trajectories for connected and automated vehicle platoon on curved roads: A two-dimensional cooperative approach

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Shengyue Yao , Yang Zhou , Bernhard Friedrich , Soyoung Ahn
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引用次数: 0

Abstract

This paper presents a cooperative two-dimensional trajectory planning algorithm for connected and automated vehicle (CAV) platoons. Specifically, the proposed algorithm generates two-dimensional optimal trajectories for CAVs with car-following relationships cooperatively within a complex road geometry. By extending the simplified Newell’s car-following model, we propose a two-dimensional Newell’s car-following model as an equilibrium car-following policy for CAVs. Based on this, a multi-objective constrained optimization is systematically formulated under a Cartesian coordinate. Due to the constraint’s complexity, a new solving algorithm based on the rapid random tree (RRT) technique is proposed. To test the effectiveness of our proposed models and algorithm, numerical simulation experiments with a real-world road geometry are conducted. Results indicate that our proposed method is able to generate trajectories for CAV platoons which are close to the equilibrium condition with smooth controls, while avoiding road obstacles. We further extend the definition of a one-dimensional car-following control string stability to a two-dimensional case. By this definition, we find that the proposed method can achieve empirical two-dimensional string stability, ensuring that both lateral and longitudinal disturbances are attenuated through vehicular strings.

在弯曲道路上规划互联自动车辆排的轨迹:二维合作方法
本文提出了一种用于联网自动驾驶汽车(CAV)排的合作式二维轨迹规划算法。具体来说,该算法可在复杂的道路几何条件下为具有汽车跟随关系的 CAV 生成二维最优轨迹。通过扩展简化的纽厄尔汽车跟随模型,我们提出了二维纽厄尔汽车跟随模型作为 CAV 的均衡汽车跟随策略。在此基础上,系统地制定了直角坐标下的多目标约束优化。由于约束的复杂性,我们提出了一种基于快速随机树(RRT)技术的新求解算法。为了检验我们提出的模型和算法的有效性,我们对真实世界的道路几何形状进行了数值模拟实验。结果表明,我们提出的方法能够在平滑控制的情况下为 CAV 排生成接近平衡条件的轨迹,同时避开道路障碍物。我们进一步将一维汽车跟随控制弦稳定性的定义扩展到二维情况。根据这一定义,我们发现所提出的方法可以实现经验上的二维串稳定性,确保横向和纵向干扰都能通过车辆串得到衰减。
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
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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