While bringing convenience to people, the growing number of vehicles on road already cause inevitable traffic congestion. Some traffic congestion happen with observable reasons, but others occur without apparent reasons or bottlenecks, which referred to as phantom jams, are caused by traditional vehicle following model. In order to alleviate the traffic instability caused by phantom jam, several models have been proposed with the development of intelligent transportation system (ITS). these have been proved to be able to suppress traffic instability in the ideal situation. But in road scenarios, uncertainties of vehicle state measurements and time delay caused by on-board sensors, inter-vehicle communications and control system of vehicles will affect the performance of the existing models severely, and cannot be ignored. In this paper, a novel predictable bilateral control model-PBCM, which consists of best estimation and state prediction is proposed to determine accurate acceleration values of the host vehicle in traffic flow to alleviate traffic instability. Theoretical analysis and simulation results show that our model could reduce the influence of the measurement errors and the delay caused by communication and control system effectively, control the state of the vehicles in traffic flow accurately, thus achieve the goal of restrain the instability of traffic flow.

中文翻译：

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缓解交通不稳定的车辆控制模型

在给人们带来便利的同时，道路上越来越多的车辆已经导致不可避免的交通拥堵。某些交通拥堵发生的原因很明显，而其他交通拥堵却没有明显的原因或瓶颈，即所谓的幻影拥塞，是由传统的车辆跟踪模型引起的。为了减轻幻影卡塞引起的交通不稳定，随着智能交通系统（ITS）的发展，提出了几种模型。这些已被证明能够在理想情况下抑制流量不稳定。但是在道路场景中，由车载传感器，车辆间通信和控制系统引起的车辆状态测量和时间延迟的不确定性将严重影响现有模型的性能，并且不能忽略。在本文中，提出了一种由最佳估计和状态预测组成的新型可预测双边控制模型PBCM，用于确定本车在交通流中的准确加速度值，以减轻交通不稳定性。理论分析和仿真结果表明，该模型可以有效减少通信控制系统引起的测量误差和时延的影响，准确控制交通流中的车辆状态，从而达到抑制交通流不稳定的目的。 。