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.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Stability of connected and automated vehicles platoon considering communications failures

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical system.CAVs can communicate with each other and exchange information.However,communication failures can change the platoon system status.To characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this work.Then,a stability analysis method is explored.Finally,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology scenarios.The results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve stability.Moreover,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

考虑CAV自主停车行为的混合交通均衡配流模型

为了评估网联自动驾驶汽车(CAV)的自主停车行为对交通系统效率的影响,建立了CAV通勤流、人工驾驶汽车(HDV)通勤流、CAV自主停车流3类交通流混行下的交通均衡配流模型.通过引入CAV停车需求内生变量,考虑CAV对路段通行能力的提升效应,从而定量描述CAV停车需求分布以及3类交通流在路段上混行的拥挤效应.分别采用用户均衡、随机用户均衡原则描述CAV、HDV出行者的路径选择行为,采用Logit离散选择模型描述自主停车CAV的停车场选择行为,由此建立多用户混合交通均衡条件以及等价的变分不等式(VI)模型.由于模型中CAV自主停车需求为未知的内生变量,提出一种改进的相继加权平均法求解该模型.最后,通过算例验证了混合交通均衡配流模型及求解算法的有效性.

Impacts of automated passenger cars on the capacity of a freeway basic section: applicability in the determination of vehicle adjustment factors in mixed traffic

The highway capacity manual(HCM)provides a formula to calculate the heavy vehicle adjustment factor(fHV)as a function of passenger car equivalent factors for the heavy vehicle(ET).However,a significant drawback is that the methodology was established solely based on human-driven passenger cars(HDPC)and human-driven heavy vehicles(HDHV).Due to automated passenger cars(APCs),a new adjustment factor(fAV)might be expected.This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on fHV and fAV values.The simulation determined observed adjustment factors at different percentages of HDHVs and APCs(fobserved).The HCM formula was used to calculate(fHCM).Modifications to the HCM formula are proposed,and vehicle adjustment factors due to HDHVs and APCs were calculated(fproposed).Results showed that,in the presence of APCs,while fobserved and fHCM were statistically significantly different,fobserved and fproposed were statistically equal.Hence,this study recommends using the proposed formula when determining vehicle adjustment factors(fproposed)due to HDHVs and APCs in the traffic stream.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

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.

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.

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。

面向出口匝道分流的协同换道策略研究

【目的】提高网联高速出口匝道路段通行效率,降低交通事故风险,保障分流车辆通行秩序。【方法】针对出口匝道上游智能网联车辆(connected automated vehicle,CAV)的换道行为所导致的交通紊乱问题,提出一种协同换道策略。兼顾通行效率和舒适度,以研究时段内所有CAV平均速度、平均加速度变化率的加权和最小为目标,以速度、加速度、加速度振动、换道起点与分流点的纵向距离等为约束,构建CAV动态速度协同优化模型,有计划地优化每个时段每辆CAV的速度。采用Gurobi优化器求解协同控制模型,并使用SUMO软件建立仿真场景评估协同控制效果。【结果】与无控制情形相比,所提出的协同方法在不同总流量和分流比例下能使车辆平均速度最高提高17.7%,总延误降低75.9%以上,平均加速度变化率改善9.3%以上;当分流比例一定时,一定总流量情况下,总流量越高平均速度、平均加速度变化率改善效果越好;在安全换道所要求的最小纵向距离约束下,出口匝道路段通行效率最高。【结论】在不同总流量和分流比例下,协同策略可为换道车辆创造换道间隙,改善通行效率,提高乘客舒适度。

考虑智能网联车辆影响的八车道高速公路施工区可变限速控制方法

为提升车联网环境下高速公路施工区交通运行效率及安全水平,提出了一种基于强化学习的可变限速控制方法.选取智能驾驶模型和真车试验模型,分别对传统人工车辆和智能网联车辆的跟驰行为进行建模,构建了以瓶颈下游路段交通流量为效率指标、瓶颈路段速度标准差为安全指标的复合奖励值,利用深度确定性策略梯度算法,分车道动态求解最佳限速值.仿真结果表明,所提可变限速控制方法在不同智能网联车辆渗漏率条件下均能有效提升交通流运行效率和安全水平,且在智能网联车辆渗漏率较低时,提升效果更加显著.当智能网联车辆渗漏率为1.0时,瓶颈下游路段交通流量提升10.1%,瓶颈路段速度标准差均值下降68.9%;当智能网联车辆渗漏率为0时,瓶颈下游路段交通流量提升20.7%,瓶颈路段速度标准差均值下降78.1%.智能网联车辆的引入能够提升至多52.0%的瓶颈下游路段交通流量.

车路协同下的无信号交叉口车辆通行调度策略

无信号交叉口车辆通行调度问题是智能交通领域的研究重点,由于车辆通行顺序决策问题的解空间随着车辆数增加而指数级增长,在保证实时性的同时找到较优通行顺序成为无信号交叉口通行调度的一大问题。针对该问题提出一种基于自适应蒙特卡罗树搜索算法的无信号交叉口车辆通行调度方法,采用分层式框架,上层集中式顺序决策,下层分布式轨迹规划。首先,建立基于冲突点的交叉口模型,将网联车加入到待搜索队列中,根据交叉口中的车辆通行特点设计通行顺序的蒙特卡罗树搜索流程,以总通行时间为指标建立评价函数,然后针对不同交通情景设计自适应探索系数及其他超参数,使算法在求解不同车辆数时以及搜索的不同时期保持最佳状态。轨迹规划环节以加速度二范数为目标函数,以速度、加速度以及始终点位置等为约束,建立最优控制命题求解车辆轨迹。最后,进行实验,结果表明该算法相较于其他算法在数值仿真和微缩平台实验中最大优化幅度分别达到33.42%和38.04%,为无信号交叉口车辆通行调度提供了一个有效解决方案。

基于MSIGJO的网联商用车换道轨迹规划方法

针对网联商用车换道安全性、平顺性较低的问题,提出一种基于多策略改进金豺优化算法(multi-strategy improved golden jackal optimization,MSIGJO)的网联商用车换道轨迹规划方法。首先,基于V2X(vehicle to everything)技术获取智能网联商用车周围状态信息,建立商用车换道安全距离模型;其次,引入商用车换道平顺性、经济性和换道效率作为指标,构建多目标协同优化函数;最后,引入动态权重位置更新策略和翻转策略改进金豺优化算法(golden jackal optimization,GJO),进而提出MSIGJO算法,利用MSIGJO算法求解函数得到最优换道轨迹。研究结果表明:该方法在商用车换道过程中横向跟踪精度提升了12.67%,侧向加速度变化率和质心侧偏角变化率分别降低了11.94%和12.65%,有效提升智能网联商用车换道安全性和平顺性,为智能网联商用车换道轨迹规划研究提供参考。

智能网联汽车多目标预测优化换道决策方法

换道决策是智能网联汽车的核心难题之一,其面临着高动态、复杂交通场景下需要综合考虑行驶安全及效率等目标的巨大挑战。提出一种多目标预测优化的换道决策方法,主要包括动力学矩阵建模及多目标预测优化问题解算。基于智能网联汽车的通讯大数据信息构建交通流矩阵模型,然后分别设计表征车辆换道安全、行驶效率的动力学模型,通过多目标综合预测优化方法,求解条件约束下预测优化问题从而优化出最优换道决策指令。结果表明,所提出的预测优化换道方法较其他方法提高了智能汽车的行驶安全性和效率。

智能网联汽车L2部分驾驶自动化监管体系研究

L2部分驾驶自动化(国内称“组合驾驶辅助”)技术加速演进、市场渗透快速提升,是智能网联汽车当前落地应用重要方向,也是我国政府当前关注的焦点,但我国当前缺少强制性的政策法规约束,亟需强化监管。通过深入研究美、欧、日等国家和地区针对智能网联汽车L2部分驾驶自动化相关政策法规及标准,充分吸收借鉴有益经验,同时系统分析我国组合驾驶辅助市场现状、技术水平和应用需求,为我国科学、有效提出组合驾驶辅助监管政策提供智力支撑。

Continuum modeling of freeway traffic flows:State-of-the-art,challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.

Integrated safety and security enhancement of connected automated vehicles using DHR architecture

Safety and security are interrelated and both essential for connected automated vehicles(CAVs).They are usually investigated independently,followed by standards ISO 26262 and ISO/SAE 21434,respectively.However,more functional safety and security fea-tures of in-vehicle components make existing safety mechanisms weaken security mechanisms and vice versa.This results in a dilemma that the safety-critical and security-critical in-vehicle components cannot be protected.In this paper,we propose a dynamic heterogeneous redundancy(DHR)architecture to enhance the safety and security of CAVs simultaneously.We first investigate the current status of integrated safety and security analysis and explore the relationship between safety and security.Then,we propose a new taxonomy of in-vehicle components based on safety and security features.Finally,a dynamic heterogeneous redun-dancy(DHR)architecture is proposed to guarantee integrated functional safety and cyber security of connected vehicles for the first time.A case study on an automated bus shows that DHR architecture can not only detect unknown failures and ensure functional safety but also detect unknown attacks to protect cyber security.Furthermore,we provide an in-depth analysis of quantification for CAVs performance using DHR architecture and identify chal-lenges and future research directions.Overall,integrated safety and security enhancement is an emerging research direction.