Communication-free Distributed Control Algorithm for autonomous vehicles at intersections

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Alireza Soltani, David M. Levinson, Mohsen Ramezani
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引用次数: 0

Abstract

This paper introduces a novel approach for managing autonomous vehicles at signal-free intersections through a Communication-free Distributed Control Algorithm (CfDCA). Unlike centralized systems or communication-based decentralized methods, CfDCA relies solely on onboard sensors and in-vehicle decision-making to ensure efficient and collision-free navigation. The algorithm formulates intersection management as a distributed optimization problem with demonstrated safety logics and robustness to measurement errors. The algorithm combines a dynamic resource acquisition graph with a refined priority function and an adaptive tolerance mechanism to ensure efficient performance under varying traffic conditions. A stochastic tie-breaking mechanism is proposed to handle rare cases of identical priorities, while deadlock prevention is guaranteed through strict priority ordering. Simulation experiments demonstrate that CfDCA reduces average delay and queue length and is able to achieve throughput higher than actuated signalized intersections and outperforms a first-come-first-served baseline in delay reduction. Additionally, the algorithm’s distributed design offers scalability and eliminates dependency on communication infrastructure.
交叉口自动驾驶车辆无通信分布式控制算法
本文介绍了一种通过无通信分布式控制算法(CfDCA)管理无信号交叉口自动驾驶车辆的新方法。与集中式系统或基于通信的分散方法不同,CfDCA完全依赖车载传感器和车载决策来确保高效和无碰撞的导航。该算法将交叉口管理作为一个分布式优化问题,具有良好的安全逻辑和对测量误差的鲁棒性。该算法结合了动态资源获取图、精细的优先级函数和自适应容错机制,保证了在不同流量条件下的高效性能。提出了一种随机打破机制来处理罕见的相同优先级的情况,同时通过严格的优先级排序来保证死锁的预防。仿真实验表明,CfDCA减少了平均延迟和队列长度,能够实现比驱动信号交叉口更高的吞吐量,并且在延迟减少方面优于先到先服务基线。此外,该算法的分布式设计提供了可扩展性,并消除了对通信基础设施的依赖。
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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