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Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

机译:在完全连接的自动驾驶汽车环境中开发无信号头的交叉口控制逻辑

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摘要

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.
机译:在车辆和交通基础设施之间建立有效的合作关系,可以提高城市交通网络中的出行可靠性。然而,缺乏合作加剧了交通拥堵,这主要是由于在信号交叉口频繁停车。有益的是,开发一种控制逻辑,该逻辑收集来自正在接近的已连接和自动驾驶车辆的基本安全消息,并通过安全且无事故的车辆操纵确保有效的交叉路口操作。在本文中,无信号头的交叉路口控制逻辑被公式化为旨在最大化交叉路口吞吐量的动态编程模型。提出了一种基于蒙特卡洛树搜索算法的随机超前技术,以确定随时间变化的近似最优动作(即加速度),以防止运动冲突。我们的数值结果证实了所提出的技术可以有效解决问题并解决现有交通信号的后果。所提出的方法虽然完全避免了交叉路口的事故,但是在各种需求模式下,大大减少了交叉路口的旅行时间(与固定时间和全驱动控制策略相比,介于59.4%和83.7%之间)。

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