考虑后视和多前车信息反馈的车辆跟驰模型

为改进车联网环境下车辆跟驰模型的稳定性,在经典OVCM模型基础上考虑后视效应、多前车速度差和多前车最优速度记忆综合信息对交通流稳定性能的影响,提出一种基于后视和多前车信息反馈的扩展车辆跟驰模型。根据线性稳定性分析法得出模型的中性稳定性判断条件,并进行数值仿真实验与分析。实验结果表明,在扰动初始条件设置一致下,所提模型相比于OV、FVD、OVCM模型,交通流稳定区域增大,速度波动幅度减小,特别是考虑的前车数k、后视敏感系数λi和记忆效应敏感系数γi取值为k=3,λi=［0.2,0.15,0.1］,γi=［0.1,0.08,0.06］时,车辆的平均速度波动率低于0.1%,由此说明,所提模型能有效减少扰动影响,增强交通流的稳态保持。     

基于多前车信息融合的智能网联车辆跟驰模型

为了进一步提高交通流的稳定性,在经典基于驾驶员记忆的最优速度（OVCM）模型的基础上,提出了一种基于多前车最优速度与紧邻加速度（MHOVA）的智能网联车辆跟驰模型。首先,引入k辆前车的最优速度变化量与紧邻前车的加速度改进OVCM模型,并分别以参数γ和ω表示其权重;然后,结合改进模型利用线性稳定性分析获得交通流的临界稳定条件;最后,利用Matlab对车队施加扰动后的速度和车头距等参数进行数值模拟与分析。仿真结果表明：在车队启动和停止过程的仿真中,所提模型比OVCM模型使得车队整体达到稳定状态的时间更短;在环形道路上车队施加扰动的仿真中,所提模型相比于全速度差（FVD）模型、OVCM和多前车最优速度（MHOV）模型,在合理加速度敏感系数ω和前车数k约束下的速度和车头距波动幅度相对较小,尤其当ω为0.3且k为5时车辆速度的向上和向下波动率最小可达0.67%和0.47%,表明改进模型能较好地吸收交通扰动和增强车队行驶稳定性。     

考虑时延速度差和限速信息的智能网联车跟驰模型

针对由于驾驶员对于道路限速和时延信息获取的不确定性而引起的跟驰行为受扰和交通流失稳等问题,提出了一种车联网（IoV）环境下考虑时延速度差和限速信息的跟驰模型TD-VDVL。首先,引入时延导致的速度变化量和道路限速信息对全速差（FVD）模型进行改进;然后,利用线性谱波微扰法推导出TD-VDVL模型的交通流稳定性判断依据,并分析模型中各参数对系统稳定性的影响;最后,利用Matlab进行数值仿真实验与对比分析。仿真实验中,分别选取在笔直道路和环形道路,给行驶过程中的车队施加轻微扰动。当条件一致时,TD-VDVL模型比优化速度（OV）、FVD模型中车队的速度波动率和车头间距起伏均小,尤其是当限速信息的敏感系数取0.3、时延速度差的敏感系数取0.3时,所提模型的车队速度平均波动率在时间500 s时可以达到2.35%,车头间距波峰波谷差仅为0.019 4 m。实验结果表明,TD-VDVL模型在引入时延速差和限速信息后,具备更优的稳定区域,能够明显增强跟驰车队吸收扰动的能力。     

基于后向和多前车效应的跟驰建模及仿真

为了能够较为准确地刻画V2V(Vehicle to Vehicle)环境下车辆间的跟驰行为特征,同时提高其在运行过程中的稳定性,在OV模型和FVD模型基础上,综合考虑相邻后车及多前车效应下的优化速度和速度差等因素,提出具有后向和多前车综合效应的跟驰模型。对所建立的跟驰模型进行线性稳定性分析,推导出模型的临界稳定性条件,进而分别讨论了不同参数取值对模型稳定性的影响,最后基于Matlab进行了相应的数值仿真验证。仿真结果表明,模型中各影响系数在允许范围内的取值越大,模型的稳定区域也相应扩大,车辆可以在更大范围内平稳行驶;模型的抗扰能力更强,在运行过程中能够自行消散微小扰动,证明模型具有较高的稳定性,能够全面的描述V2V环境下交通流所呈现出的运行规律。     

一种改进全速度差模型的稳定性分析与仿真

针对传统全速度差（FVD）模型缺乏考虑前车最优速度影响的局限性,提出了一种改进的全速度差（IFVD）模型。在IFVD模型中,除了考虑跟驰车自身的最优速度和前车的速度差外,还进一步分析了多辆前车的最优速度信息对提高交通流稳定性的作用。并利用小振幅扰动法对模型的稳定性进行了分析,推导了模型的临界稳定性条件。最后通过数值模拟方法对FVD和IFVD模型进行了对比研究。研究结果表明,在相同的初始扰动条件下,考虑前车最优速度影响的IFVD模型能更有效地抑制交通流的堵塞,且随着考虑前车数量的增多,自由流稳定的灵敏度系数临界值在减小,稳定区域逐渐增大。     

考虑前车动态效应的高速跟驰交通流研究

为描述交通中存在的“高速跟驰”现象,在NaSch模型的基础上,考虑了前车的运动特性,并结合驾驶员的驾驶行为差异,建立了考虑前车动态效应的高速跟驰交通流模型（DPM）。通过数值模拟得到了高速跟驰规律,当道路车流密度为0.18时,车道上的高速跟驰率为4.93%;同时,通过分析车辆运动的时空特性,模拟出交通流中自由流、同步流以及宽幅运动阻塞现象;还得出了不同驾驶员占比下的速度-流量-密度的关系;并分析了车辆随时间变化的速度及车头间距波动情况,较NaSch模型有更高的交通稳定性。通过NGSIM数据集及国内实测数据验证了DPM模型的有效性和实用性。     

考虑前车加速度信息的跟驰模型及数值模拟

为了改进最优速度（OV）跟驰模型只考虑车间距因素对车辆跟驰行为影响的不足,在考虑前导车加速度信息对后车跟驰行为影响的基础上,提出了一种改进OV跟驰模型。并利用小振幅扰动法对模型进行了线性稳定性分析,得到了模型的稳定性条件,对改进OV模型进行了数值模拟。仿真结果表明,与传统OV模型相比,考虑前导车加速度信息对跟驰车的影响后,交通流的稳定区域显著增加,改进OV模型能有效抑制交通流的堵塞。     

考虑时延的智能网联汽车混合交通流稳定性分析

为研究时延对交通流稳定性的影响,构建考虑时延的人工驾驶汽车和智能网联汽车混合交通流稳定性分析模型.首先,分析并确定混合交通流中不同类型跟驰模式的比例关系和时延取值;然后,在此基础上采用不同的跟驰参数和时延值区分车辆的跟驰模式,并由此推导出混合交通流线性稳定条件;最后,以智能驾驶员模型为例,通过设计数值实验分别讨论智能网...     

考虑后视和最优速度记忆的跟驰模型及仿真

为提高交通流的稳定性,在考虑后视效应和速度差信息（Backward Looking and Velocity Difference,BLVD）模型的基础上,综合考虑后视和最优速度记忆效应,提出了一个扩展的跟驰模型。采用线性稳定性分析,推导出该模型的交通流稳定判据,发现在模型中引入后视和最优速度记忆效应的共同作用后,交通流的稳定区域有明显增大。通过数值仿真验证了理论分析,仿真结果表明：在初始扰动相同的条件下,与BLVD模型相比,新提出的扩展模型具有更好的交通流致稳性能。最后,使用NGSIM数据对所提出的跟驰模型进行参数标定和评价,证明其能更准确地刻画车流演变规律。     

前后车辆最优速度差跟驰模型与数值仿真

为了克服经典最优速度（OV）模型仅依据车间距单一因素调整跟驰车最优速度的局限性,在考虑前导车与跟驰车最优速度差信息对交通流稳定性影响的基础上,提出了一种改进的最优速度差（OVD）模型,并通过小振幅摄动法对模型进行了稳定性分析,推导了模型的稳定性条件。最后对改进OVD模型进行了数值仿真。通过线性稳定性分析,发现考虑前后车辆最优速度差后,自由流稳定的临界驾驶员反应灵敏度系数明显减小,稳定区域显著增加。仿真结果表明,与OV跟驰模型相比,在相同初始扰动条件下,改进OVD模型能够明显增强交通流的稳定性。     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

Stability analysis of a general nonlinear car-following model

ABSTRACT

In order to study the traffic jams and congestion, this paper investigated the stability of a car-following model on the basis of synchronisation theory of complex network. By using the Lyapunov stability theory and designing the appropriate controller, the car-following model is quickly stabilised and the stability condition of the model is obtained. In addition, the stability of car-following model is studied when the vehicle is subjected to external stochastic disturbance. Finally, the numerical simulation is carried out by using the MATLAB simulation technology, and the results show that the car-following model is rapidly stabilising, and congestion phenomenon is effectively alleviated under the controller designed.     

T-CPS下考虑人驾车行为影响的混行车辆协同控制

由于传统人驾车(traditional human-driven vehicles,HVs)驾驶行为会受到驾驶员的心理和生理活动的不确定性影响,可能使得车辆频繁地加减速,进而导致混合交通条件下网联自动车(connected and automated vehicles,CAVs)很难快速跟踪此行为.针对这一问题,首先提出一种提前预测传统人驾车行为的组合神经网络.在此基础上,考虑通信时延和车辆运动学特性,设计一种基于交通信息物理系统(transportation-cyber physical system,T-CPS)的混行车群内车辆协同控制策略,使其能够快速跟踪上传统人驾车行为,并对混行车群内网联自动车之间的串稳定性进行分析.最后,在混合交通条件下设置由1辆传统人驾车、1辆领头网联自动车和4辆跟随网联自动车形成的混行车群,利用下一代交通仿真(next generation simulation,NGSIM)车辆轨迹数据选出高质量传统人驾车状态,并通过仿真实验验证所提协同控制策略的有效性和可行性.由仿真实验结果可知,所提协同控制策略可以保证所有的网联自动车能够快速跟踪上传统人驾车行为,为解决新型混合交通带来的新问题提供一定的理论指导和借鉴.     

A model identification scheme for driver-following dynamics in road traffic

The driver-following, or car-following, model is one of the most fundamental driver behavior models that are applied in intelligent transport applications. Its fidelity determines the applicability of microscopic traffic simulators, where the model is often implemented to mimic real traffic. Meanwhile, the behavioral model is fundamental to the development of advanced driving assistance systems (ADAS). This paper develops a dynamic model identification approach based on iterative usage of the extended Kalman Filtering (EKF) algorithm. Among other things, this allows to carry out model identification using a rather general optimization objective on the whole physical states of the following vehicle. In particular, the method is established on the basis of the equivalence between the Kalman filter and the recursive least squares (RLS) method in a specific context of parameter identification. To illustrate the method, two car-following models are studied in numerical experiments using real car-following data. The method has shown advantages in replication and prediction of vehicle dynamics in car-following over the conventional approaches. It has also the potential to be further extended for building tactical driving controllers in intelligent transportation applications.     

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.