Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Proactive Platooning Based on C-V2X to Relieve Congestion at a Signalized Intersection

Nowadays urban traffic congestion becomes a major issue due to the increase in vehicles that lead to more commuting time,accidents,traffic violations,and high fuel consumption.Platooning is a good way to solve traffic congestion because it drives with a smaller inter-vehicle distance than human-driving.This paper proposes proactive platooning based on C-V2X(cellular vehicle-to-everything)to relieve congestion.Proactive platooning reduces the inter-vehicle distance and increases the throughput of signalized intersections through integrating the networked control platooning and computation-centralized platooning.Model and simulation experiments of proactive platooning were conducted to verify the impact of inter-vehicle distance on platooning latency,platooning safety,and traffic throughput.Simulation results show that the optimal inter-vehicle distance under proactive platooning is less than half of human-driving at a signalized intersection.

Large-Scale Vehicle Platooning:Advances and Challenges in Scheduling and Planning Techniques

Through vehicle-to-vehicle(V2V)communication,autonomizing a vehicle platoon can significantly reduce the distance between vehicles,thereby reducing air resistance and improving road traffic efficiency.The gradual maturation of platoon control technology is enabling vehicle platoons to achieve basic driving functions,thereby permitting large-scale vehicle platoon scheduling and planning,which is essential for industrialized platoon applications and generates significant economic benefits.Scheduling and planning are required in many aspects of vehicle platoon operation;here,we outline the advantages and challenges of a number of the most important applications,including platoon formation scheduling,lane-change planning,passing traffic light scheduling,and vehicle resource allocation.This paper’s primary objective is to integrate current independent platoon scheduling and planning techniques into an integrated architecture to meet the demands of large-scale platoon applications.To this end,we first summarize the general techniques of vehicle platoon scheduling and planning,then list the primary scenarios for scheduling and planning technique application,and finally discuss current challenges and future development trends in platoon scheduling and planning.We hope that this paper can encourage related platoon researchers to conduct more systematic research and integrate multiple platoon scheduling and planning technologies and applications.

Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Inspired by mobile edge computing(MEC),edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center.However,the quality of model training will be affected by the limited communication and computing resources of network edge.In this paper,how to improve the training performance of a federated learning system aided by intelligent reflecting surface(IRS)over vehicle platooning networks is studied,where multiple platoons train a shared federated learning model.Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon.Meanwhile,IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner.Firstly,the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling,bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon.Specif-ically,in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks.Then,a method based on sequential optimization algorithm(SOA)and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided commu-nication,respectively.Finally,a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons.Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks.The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.

外部干扰和随机DoS攻击下的网联车安全H∞队列控制

针对网联车队列系统易受到干扰和拒绝服务(Denial of service, DoS)攻击问题,提出一种外部干扰和随机DoS攻击作用下的网联车安全H∞队列控制方法.首先,采用马尔科夫随机过程,将网联车随机DoS攻击特性建模为一个随机通信拓扑切换模型,据此设计网联车安全队列控制协议.然后,采用线性矩阵不等式(Linear matrix inequality, LMI)技术计算安全队列控制器参数,并应用Lyapunov-Krasovskii稳定性理论,建立在外部扰动和随机DoS攻击下队列系统稳定性充分条件.在此基础上,分析得到该队列闭环系统的弦稳定性充分条件.最后,通过7辆车组成的队列系统对比仿真实验,验证该方法的优越性.

网联自动驾驶货车编队规划与控制研究综述

物流运输联盟内开展货车编队运行,有望成为未来物流运输降低运营成本的新形态。本文以网联自动驾驶货车编队规划与控制为研究对象,通过文献检索来分析网联自动驾驶货车编队的关键技术与研究现状。从市场应用角度,分析目前实现货车编队市场应用面临的挑战,以及相关利益方之间的成本分配问题。从技术方法视角,围绕网联自动驾驶编队规划与运作控制问题,解读相关技术内涵与实现方法。通过评述网联自动驾驶编队技术研究现状,总结该领域未来研究方向,旨在为研究者掌握该方向发展脉络提供参考。

跟驰事件下网联混驾编队的运行特征及生态安全影响

考虑当前自动驾驶技术成熟度和公众接受程度等问题,网联混驾编队模式可能是自动驾驶编队全面普及应用前的可行的过渡模式之一。为研究网联条件和车队形式对网联混驾编队运行特征及生态安全的影响,文中基于驾驶模拟技术搭建的网联混驾编队实验测试平台,构建由5辆车组成的“领航车-网联L2级人工驾驶车辆、跟驰车-网联自动驾驶车辆”的网联混驾编队模式,并邀请36名被试开展驾驶模拟实验。考虑时域分析侧重呈现指标的时间维度变化,而频域分析可以挖掘指标的频率分布特性,文中选取速度、加速度指标从时域和频域维度分析车辆运行特征,同时选取油耗、速度安全熵指标评价车辆运行的生态安全性。结果表明:网联条件和编队模式均有助于车辆行驶更加平稳,并显著提升其生态安全性;网联混驾编队的综合效能最佳,相较传统单车模式,油耗可降低10.67%,速度安全熵值可降低73.25%。该研究成果可为自动驾驶企业及行业开展网联人机交互终端设计、领航驾驶辅助系统研发、网联混驾编队可行性及有效性测试等提供方案借鉴和平台支持。

基于信息传输时变时延的车辆队列纵向稳定性控制算法

智能网联车辆队列行驶面临复杂的交通环境,所引发的时延、丢包等信息传输问题将导致队列车辆行驶稳定性降低而亟待解决.针对复杂交通环境,引入信息新鲜度(age of information, AoI)并提出了一种适应时变时延的智能网联车辆队列行驶稳定性控制算法.该控制算法根据队列中多前车信息新鲜度来调整其对队列车辆车头间距影响的权重,同时依据时变时延信息预测队列中跟驰车辆与前车的车头间距,队列车辆按照请求周期向路侧单元(road side unit, RSU)实时发送请求,RSU依据车辆间距从小到大依次回应请求队列中的各个车辆,以控制其因时变时延可能造成的碰撞.数值仿真结果显示,相对智能驾驶员模型(intelligent driver model, IDM)而言,所提出的队列纵向稳定性控制算法具有更好的控制效果.针对时变时延发生概率20%的车车通信,队列车辆车头间距偏差的降低比例达6.4%,平均峰值信息新鲜度的降低比例达8.7%.同时,分析了队列车辆请求周期和回应请求车辆数量对队列纵向稳定性的影响,随着两者数值增加,控制算法给出的队列纵向稳定性分别呈现降低和增加的趋势.最后,实车测试了队列切出场景下车辆行驶数据和车辆接收信息的时延数据,将其引入数值仿真实验中.结果表明,车头间距偏差降低比例达15%.

考虑时延的异质协作车队事件触发控制研究

针对目前研究较少考虑车辆混行造成的异质问题与时延问题,本文提出了一种具有时延的异质协作式自动驾驶车队事件触发控制方法 .首先,建立了考虑混行的异质车辆时延模型;其次,基于PID(proportional-integral-derivative)控制与模型预测控制(MPC),设计了不同模式下的事件触发控制器;然后,在城市和紧急情况下,对所提出的控制器进行了仿真分析;最后,基于Jetson Nano模型车进行了实车实验.仿真与实验结果表明,所提出的事件触发控制方法能够在不同情况下更好地权衡控制精度与计算速度间的矛盾.在带有通信时延的紧急情况下,仍能够保持较低的误差.

基于MPC的智能网联卡车队列最优能耗控制

针对高速公路智能网联卡车队列的能耗优化问题,提出了一种基于模型预测控制的双层控制算法。算法由速度规划层和跟踪控制层两部分构成,其中速度规划层结合道路坡度及车辆状态,通过线性能量守恒模型来消除动力学模型中的非线性项,并以发动机输出功率作为油耗优化指标,采用线性模型预测控制方法获得车队的规划速度;为减少简化车队模型引起的不确定性,跟踪控制层采用基于线性矩阵不等式的分布式鲁棒模型预测控制算法,设计状态反馈控制器对上层规划的最优速度进行跟踪。仿真结果验证了上述双层控制算法的可行性和有效性,可以在保证最优能耗控制的前提下实现车队安全稳定行驶。

基于实测数据的货车编队荷载作用特征分析

货车编队技术是货运交通智能化的重要研究方向之一。以货车编队的荷载作用为研究对象,在明确货车编队的横向、纵向控制模式的基础上,调研并总结了国内外货车编队的实测项目;基于AUTOPILOT项目的实测数据,分析了货车编队的横向和纵向荷载分布特征,并在此基础上分析了货车编队的轴载作用影响。结果表明:货车编队的轮迹横向分布标准差为7.14 cm,仅为传统货车的1/3,具有明显的“渠化”特征;货车编队在以100 km/h的速度行驶时,车头时距可低于0.8 s,约为相同速度下传统货车的27%;与传统货车相比,货车编队运行轴载作用次数具有“高频”特性,对现有路面结构的抗疲劳性能提出更高的要求。

基于扰动预测的网联车鲁棒协同巡航预测控制

针对车辆巡航过程中前车未来加速度不确定性引起的控制问题,提出了一种用于解决网联车巡航问题的鲁棒模型预测控制(Model predictive control,MPC)算法。采用车辆误差模型,将前车预测加速度与实际加速度之间的偏差视为加性扰动。根据已知的物理约束,先通过滚动预测的方法得到更紧凑的扰动多面体,然后对原系统的约束进行紧缩处理以降低传统tube MPC的保守性。基于传统tube MPC在控制器中引入前馈补偿,通过l2增益特性条件得到相应的线性增益,提出一种基于滚动扰动预测的tube MPC算法,并且证明了该算法是输入到状态稳定的。通过5辆车的数值仿真,验证了该算法的有效性。

网联车辆编队的信号灯路口通行控制策略

为提高信号灯路口场景下车辆编队的安全性和通行效率,提出V2X环境下网联车辆编队的信号灯路口通行控制策略。在信号灯路口上游设置一段控制区,以车辆编队领航车在信号灯路口排队疏散后到达信号灯路口为目标,对进入控制区的车辆编队速度进行优化。考虑速度变化对车辆编队纵向安全距离的影响,引入分布式模型预测控制理论,建立固定跟车时距控制策略下编队车辆的速度跟随控制模型,并采用加权二次型性能泛函将速度跟随控制问题转化为易于在线求解的凸二次规划问题。通过PreScan/Simulink软件仿真将信号灯路口通行控制策略与固定跟车时距控制策略进行对比,以验证速度跟随控制模型的有效性,结果表明:信号灯路口通行控制策略能够避免车辆间碰撞冲突,并可以减少编队车辆在信号灯路口的通行耗时,在保证安全性的同时提高了通行效率。

考虑编队意愿的CAV队列对混合交通流条件下交叉口通行能力的影响研究

为研究考虑编队意愿的网联自动驾驶车辆(CAV)队列对混合交通流条件下交叉口通行能力的影响,针对CAV、自动驾驶车辆(AV)、人工驾驶车辆(HDV)构成的混合交通流,考虑CAV的编队意愿,基于10种车辆跟随状态类型和4种车间时距类型,基于马尔可夫链理论,推导出车辆跟随状态类型的状态转移矩阵和状态概率分布,构建交叉口通行能力模型,分析队列时距、编队意愿、渗透率(P_(C))、最大队列规模对交叉口通行能力的影响并进行数值案例验证。研究结果表明:通行能力随CAV队列时距的减小而增加;在给定P_(C)时,通行能力随编队意愿的增强而增加;P_(C)小于0.2时,编队意愿对交叉口通行能力的提高很小,P_(C)大于0.4时,编队意愿对提高交叉口通行能力的影响变得显著;更高的P_(C)是否会带来更大的通行能力,取决于编队意愿和异质的车间时距;对于控制队列规模不超过一个最大值k_(m′),在给定P_(C)时,通行能力随最大队列规模的增大而增加,但增幅逐渐减小;P_(C)小于0.2时,提高最大队列规模对通行能力的提高较小,P_(C)大于0.6时,最大队列规模对提高通行能力的影响变得显著。

智能网联车专用道内队列控制模型

在智能网联车与人工驾驶车辆混合通行的场景中,为提高混行交通流的通行效率,提出了一种智能网联车专用道内车辆按编队行驶的队列控制模型。根据快速路道路条件和智能网联车的运行模式,通过分析智能网联车专用道内前后两车的运动过程,构建了智能网联车车队创建、加入和巡航控制模型,并求解出专用道内智能网联车形成编队行驶的最优策略。为验证队列控制模型的有效性,利用SUMO仿真平台搭建单车道和多车道仿真场景,模拟智能网联车在专用道内形成车队的过程,统计分析车流的通行效率,其中单车道仿真结果显示,相比于传统CACC跟驰模型,队列控制模型能将车辆编队时间减少23.06%;多车道仿真结果显示,当CAV渗透率在23.22%~40%时,队列控制模型下的车辆平均行程时间缩短17%。这表明在智能网联车专用道内,队列控制模型能更有效地利用专用道的空间资源,从而缩短车辆编队时间,提高智能网联环境下混行交通流的通行效率,故可用于混行交通流下智能网联车专用道的设置。

智能网联车辆组队与离队行为对混合交通流影响特性分析

针对智能网联车队的组队和离队场景从宏观基本图与微观跟驰行为2方面进行建模:首先在宏观层面,研究含有智能网联车队的混合交通流组态概率分布,紧接着对网联车渗透率p以及车队长度S这2个重要参数分别研究其对混合交通流通行能力的影响;其次,从微观角度出发,分别对车队的组队场景和分离场景进行仿真研究其微观运行状况。结果表明:当网联车渗透率为1时,道路通行能力可达到3777辆/h,是不含车队交通流通行能力的1.5倍以上;车辆组成车队所需的时间主要受车间距离以及车队规模影响;车队分离场景的效率以及稳定性均高于组队场景。研究结论可支撑未来车队动态演化过程的研究,同时也可为车队的宏观与微观运行管理提供理论支撑。