Department of Computer Science and Engineering, National Institute of Technology, Rourkela 769008, India.
Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel). 2023 Feb 23;23(5):2481. doi: 10.3390/s23052481.
Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.
全球范围内,车辆数量的增加、交通拥堵和道路事故都是严重的问题。自动驾驶汽车(AV)在车队中行驶,为高效的交通流管理提供了创新的解决方案,特别是在缓解拥堵方面,从而减少事故的发生。近年来,基于车队的驾驶,也称为车辆编队,已成为一个广泛的研究领域。车辆编队通过减少车辆之间的安全距离来减少行驶时间并增加道路容量。对于联网和自动驾驶车辆,协同自适应巡航控制系统(CACC)和车队管理系统起着重要作用。由于 CACC 系统基于车辆通信获得的车辆状态数据,因此编队车辆可以保持更近的安全距离。本文提出了一种基于 CACC 的车辆编队自适应交通流和避撞方法。所提出的方法考虑了车队的创建和演变,以在拥堵期间管理交通流并避免不确定情况下的碰撞。在行驶过程中识别出不同的阻塞场景,并提出了解决这些挑战性情况的方案。通过合并和加入操作来帮助车队的稳定运动。仿真结果表明,由于编队缓解了拥堵,显著改善了交通流,减少了行驶时间并避免了碰撞。