Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jianjie Kuang, Gangfeng Tan, Xuexun Guo, Xiaofei Pei, Dengzhi Peng
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Abstract

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three-layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision-making layer, a decision-making system based on the finite-state machine is established for formation switching. In the second layer, the lane-changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking.

Abstract Image

Abstract Image

通过切换队形实现自动重型车辆排避开障碍车辆的研究
随着自动驾驶汽车的发展,与自动驾驶汽车排(AVP)相关的研究受到越来越多的关注。AVP 被认为是缓解交通拥堵、降低车辆能耗的有效手段之一。本文研究了一种通过切换重型车辆自动排序队形来避开交通中障碍车辆的三层方法。在决策层,建立了基于有限状态机的队形切换决策系统。在第二层,基于量子多项式曲线拟合对需要变道的车辆进行变道轨迹优化。在车辆控制层,每辆车都有一个基于滑模控制的纵向控制器和一个基于模型预测控制的横向控制器,以跟踪计划轨迹完成目标编队。最后,在 MATLAB/TruckSim 中对所提出的方法进行了仿真。仿真结果表明,提出的方法可以通过切换编队有效避开障碍车辆,并且速度跟踪和轨迹跟踪的平均误差值较小。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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