A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.

A car-following model with the anticipation effect of potential lane changing

In this paper, a new car-following model is presented, taking into account the anticipation of potential lane changing by the leading vehicle. The stability condition of the model is obtained by using the linear stability theory. The modified Korteweg-de Vries （KdV） equation is constructed and solved, and three types of traffic flow in the headway-sensitivity space, namely stable, metastable and unstable ones, are classified. Both the analytical and simu- lation results show that anxiety about lane changing does indeed have an influence on driving behavior and that a consideration of lane changing probability in the car-following model could stabilize traffic flows. The quantitative relationship between stability improvement and lane changing probability is also investigated.

A new traffic model on compulsive lane-changing caused by off-ramp

In the field of traffic flow studies, compulsive lane-changing refers to lane-changing （LC） behaviors due to traffic rules or bad road conditions, while free LC happens when drivers change lanes to drive on a faster or less crowded lane. LC studies based on differential equation models accurately reveal LC influence on traffic environment. This paper presents a second-order partial differential equation （PDE） model that simulates both compulsive LC behavior and free LC behavior, with lane-changing source terms in the continuity equation and a lane-changing viscosity term in the momentum equation. A specific form of this model focusing on a typical compulsive LC behavior, the ＇off-ramp problem＇, is derived. Numerical simulations are given in several cases, which are consistent with real traffic phenomenon.

A new traffic model with a lane-changing viscosity term

In this paper, a new continuum traffic flow model is proposed, with a lane-changing source term in the continuity equation and a lane-changing viscosity term in the acceleration equation. Based on previous literature, the source term addresses the impact of speed difference and density difference between adjacent lanes, which provides better precision for free lane-changing simulation; the viscosity term turns lane-changing behavior to a ＂force＂ that may influence speed distribution. Using a flux-splitting scheme for the model discretization, two cases are investigated numerically. The case under a homogeneous initial condition shows that the numerical results by our model agree well with the analytical ones; the case with a small initial disturbance shows that our model can simulate the evolution of perturbation, including propagation,dissipation, cluster effect and stop-and-go phenomenon.

Lane changing analysis for two-lane traffic flow

In this paper, the two-lane traffic are studied by using the lane-changing rules in the car-following models. The simulation show that the frequent lane changing occurs when the lateral distance in car following activities is considered and it gives rise to oscillating waves. In contrast, if the lateral distance is not considered （or considered occasionally）, the lane changing appears infrequently and soliton waves occurs. This implies that the stabilization mechanism no longer functions when the lane changing is permitted. Since the oscillating and soliton waves correspond to the unstable and metastable flow regimes, respectively, our study verifies that a phase transition may occur as a result of the lane changing.

Reference Modelof Desired Yaw Anglefor Automated Lane Changing Behavior of Vehicle

In this paper,it studies the problem of trajectory planning and tracking for lane changing behavior of vehicle in automatic highway systems. Based on the model of yaw angle acceleration with positive and negative trapezoid constraint,by analyzing the variation laws of yaw motion of vehicle during a lane changing maneuver,the reference model of desired yaw angle and yaw rate for lane changing is generated. According to the yaw angle model,the vertical and horizontal coordinates of trajectory for vehicle lane change are calculated. Assuming that the road curvature is a constant,the difference and associations between two scenarios are analyzed,the lane changing maneuvers occurred on curve road and straight road,respectively. On this basis,it deduces the calculation method of desired yaw angle for lane changing on circular road. Simulation result shows that,it is different from traditional lateral acceleration planning method with the trapezoid constraint,by applying the trapezoidal yaw acceleration reference model proposed in this paper, the resulting expected yaw angular acceleration is continuous,and the step tracking for steering angle is not needed to implement. Due to the desired yaw model is direct designed based on the variation laws of raw movement of vehicle during a lane changing maneuver, rather than indirectly calculated from the trajectory model for lane changing, the calculation steps are simplified.

Influence of lane change on stability analysis for two-lane traffic flow

This paper deals mainly with the influence of lane changing behaviours on the stability of two-lane traffic flow under a periodic boundary condition. Following the description of an optimal velocity model for two vehicle groups and the derivation of their stability conditions, the feedback signals, which involve information about vehicles from both lanes acting on the two-lane traffic system, are introduced into the optimal velocity model. The control signals play a role in alleviating the traffic jam only if the traffic state is in congestion, and their role will vanish if the traffic state is in the steady state. The numerical simulations show that lane changing behaviours can break the steady state of two-lane traffic flow and aggravate the traffic disturbance, but the control method would successfully suppress the traffic jam eventually, which implies that the conclusions obtained here have certain theoretical and practical significance.

Enhancing Safety in Autonomous Vehicle Navigation:An Optimized Path Planning Approach Leveraging Model Predictive Control

This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.

Improved STCA Model for Multi-Lane Using Driving Guidance under CVIS

In a multi-lane area,the increasing randomness of lane changes contributes to traffic insecurity and local traffic flow instability.A study on safe lane shifting activity that focuses on threat assessment under real-time knowledge is necessary to enhance smooth vehicle flow.This paper proposed amore comprehensive lane changing guidance rule to investigate the status of surrounding vehicles to accommodate future vehicle-on-road collaborative environments based on these parameters 1)lane change demand and 2)treat assessment function.The collaborative relationships between vehicles are analyzed using a cellular automata model based on their location,velocity,and acceleration.We analyze and examine the relationship between the number of lanes and traffic flow when the road capacity is heavily mined via intelligent lane changing.Our analysis can further provide theoretical guidance for the selection of road expansion mode.Our proposed STCA-L is compared based on the average speed,average flow,lane changing frequency,spatial and temporal pattern of STCA,STCA-I,and STCA-S,and STCA-M under different vehicle densities.The numerical simulation results show that our proposed STCA-L provides themost flexible lane changing guidance in the multi-lanes road.Moreover,the simulated results show that the exponential growth of physical space cannot provide the corresponding increase in the average flow of vehicles.

交通事故下基于CA-SIR模型的高速公路拥挤传播

为探究交通事故对高速公路所造成的拥挤传播影响,将流行病传播SIR模型微观化和动态化。针对其计算求解复杂的问题,将其与元胞自动机(CA)有机结合,并考虑了高速公路事故的特点和影响范围等因素。以单向三车道为例,假设事故占用最左侧车道的4个元胞格,对后续车辆拥堵传播的动态过程进行了模拟。对于模型中关键的拥堵传播率与拥堵恢复率2个参数,则主要通过高速公路上车辆的换道概率和制动概率来设定定量化公式,最终分别得到三者间的确定关系。相关仿真结果表明:所提出的CA-SIR模型与传统的SIR模型相比,在最大拥挤车辆数保持相似的情况下,由于考虑元胞自动机的离散,可以更早反映事故发生后的车道封闭,更符合实际情况;根据相关的拥挤发生时空图,当制动概率增加时,事故带来的拥堵更早发生,更晚消散,拥堵的规模和影响路段长度更大;当换道概率降低时,对车辆最大排队长度及整体行驶时间并无显著影响,但车辆在到达事故区域后所造成的拥挤影响会更早发生,拥挤影响的车辆数也更大。故此,在高速公路交通事故发生后,应通过路侧交通广播、智能网联汽车、车路协同等技术手段,尽可能快地发布交通事故信息,引导车辆提前换道,减少制动概率,以期降低事故影响的拥挤规模和持续时间。

混合交通流环境下基于MSIF-DRL的网联自动驾驶车辆换道决策模型

现有的网联自动驾驶车辆(Connected and Automated Vehicles,CAV)换道决策模型鲁棒性较差,存在安全隐患,且单纯依赖自车信息、较小范围内的感知信息,难以在CAV与人工驾驶车辆(Human-Driven Vehicles,HDV)混行的环境中推断出最优动作.综合考虑感知信息、自车以及车-车通信(Vehicle-to-Vehicle,V2V)范围内上、下游CAV信息,提出一种混合交通流环境下集成多源信息融合的深度强化学习(Multi-Source Information Fusion Deep Reinforcement Learning,MSIF-DRL)端到端网联自动驾驶换道决策模型.首先,构建含有多源信息的状态空间,并为不同信息分配权重;其次,通过编码网络将各种动态信息编码到高维特征空间,进行信息融合得到特征图;然后,将其扁平化送入拥有优先经验回放机制的竞争双深度Q网络中,进行动作的选择和评估;最后,分别设计适用于主线、匝道CAV的奖励函数引导所提MSIF-DRL模型解决高速公路合流区驾驶场景中CAV的自由以及强制换道问题.基于SUMO软件在各种仿真条件下进行实验,将所提出的MSIF-DRL换道决策模型与现有换道模型进行比较,验证其有效性和优越性.研究结果表明:相较于现有模型,所提MSIF-DRL模型在各种仿真条件下均拥有最高的奖励值、换道成功率、合流成功率、平均行车速度、舒适性以及最低的碰撞风险,其中换道成功率、合流成功率、平均行车速度最大分别提升了29.17%、27.71%、17.43%;随着渗透率的提高,该模型在处理混合交通流环境下CAV的换道决策问题时拥有更强的性能和鲁棒性.

Traffic dynamics of an on-ramp system with a cellular automaton model

This paper uses the cellular automaton model to study the dynamics of traffic flow around an on-ramp with an acceleration lane. It adopts a parameter, which can reflect different lane-changing behaviour, to represent the diversity of driving behaviour. The refined cellular automaton model is used to describe the lower acceleration rate of a vehicle. The phase diagram and the capacity of the on-ramp system are investigated. The simulation results show that in the single cell model, the capacity of the on-ramp system will stay at the highest flow of a one lane system when the driver is moderate and careful; it will be reduced when the driver is aggressive. In the refined cellular automaton model, the capacity is always reduced even when the driver is careful. It proposes that the capacity drop of the on-ramp system is caused by aggressive lane-changing behaviour and lower acceleration rate.

Quantification of Ride Comfort Using Musculoskeletal Mathematical Model Considering Vehicle Behavior

This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has been evaluated in recent years on the basis of a subjective sensory evaluation given by the driver.However,reflecting driving sensations in design work to improve ride comfort is abstract in nature and difficult to express theoretically.Therefore,we evaluated the human body’s effects while driving scientifically by quantifying the driver’s behavior while operating the steering wheel and the behavior of the automobile while in motion using physical quantities.To this end,we collected driver and vehicle data using amotion capture system and vehicle CAN and IMU sensors.We also constructed a three-dimensional musculoskeletal mathematical model to simulate driver movements and calculate the power and amount of energy per unit of time used for driving the joints and muscles of the human body.Here,we used comfort mode and sport mode to compare damper characteristics in terms of hardness.In comfort mode,damper characteristics are soft and steering stability is mild,but vibration from the road is not easily transmitted to the driver making for a lighter load on the driver.In sport mode,on the other hand,damper characteristics are hard and steering stability is comparatively better.Still,vibration from the road is easily transmitted to the driver,whichmakes it easy for a load to be placed on the driver.As a result of this comparison,it was found that a load was most likely to be applied to the driver’s neck.This result in relation to the neck joint can therefore be treated as an objective measure for quantifying ride comfort.

基于数据驱动的快速路合流区加速车道长度的研究

设计长度合理的加速车道能有效地缓解快速路合流区频繁出现的交通瓶颈问题,因此采用数据驱动方法对快速路合流区的加速车道长度进行研究。利用无人机设备测取了快速路合流区的交通数据,从交通流特性及车辆汇入行为这两个角度对实测数据进行分析,得到了合流区车辆的驾驶行为;根据合流区交通流特点,对数据集进行聚类分析,使用生成对抗式网络训练不同合流区汇入行为车辆的跟驰换道模型,并与实测数据和SUMO仿真软件中内置模型进行对比分析;应用生成对抗式网络模型进行交通环境仿真,选取速度、交通密度、交通冲突率指标建立奖励评价函数,得出了加速车道长度设计的推荐值。研究结果表明:采用主线车辆提前减速和向内侧车道换道这两种手段,可实现协同换道避让匝道汇入的车辆;相比SUMO软件内置模型,生成对抗式网络模型更加贴近实际情况;仿真得出的单车道平行式加速车道长度分别在100、80、60 km/h情况下的推荐值为280、240、200 m。

基于航测数据的不同风格换道轨迹规划

不当的换道行为可能危及交通安全,导致交通事故和拥堵,因此有必要探索不同驾驶风格下在车道出口的换道轨迹.本文利用中国高速公路和快速路拥堵场景数据集中的车辆轨迹数据,采用K-means算法将驾驶人分为谨慎型、普通型和激进型三类;通过聚类分析和换道时间预测,以最小化换道纵向位移和行驶稳定性加权值之和为优化目标,同时以舒适性和安全性评价指标为约束条件,采用五次多项式进行最优换道轨迹规划;随后,使用遗传算法解决轨迹规划问题,基于Prescan、CarSim、MATLAB/Simulink仿真平台建立横纵向联合控制二自由度车辆动力学模型;最后,设计自车前车、目标车道前车和目标车道后车三种典型换道场景,并通过仿真实验评价不同驾驶风格下的换道轨迹规划效果和车辆轨迹跟踪控制效果.实验结果表明:在目标车道有车的场景下提出的融合驾驶风格的轨迹规划算法使得规划的换道轨迹增加了激进型驾驶风格的换道时长,同时减少了普通型和谨慎型驾驶风格司机的换道时长,进而能够确保换道过程的时效性、安全性和舒适性.

城市连续隧道交织区主线交织车辆换道点选择行为研究

基于城市连续隧道交织区分析主线交织车辆换道点选择行为,以实地采集的车行轨迹数据为基础,利用随机森林模型分析主线车辆换道点选择行为特征及影响行为决策的影响因素,而后分别采用支持向量机模型与随机森林模型进行换道点选择行为决策建模和对比分析。研究结果表明:影响主线交织车辆换道点选择的主要因素为目标车辆状态以及本车道的后车状态,且经特征筛选之后建立换道决策模型的精度更高,其中支持向量机模型可以较好的刻画换道行为,模型的预测精度不低于85%。研究成果有助于在仿真模型中更准确描述换道点选择行为,同时为城市连续隧道交织区的优化和辅助驾驶策略制定提供支撑。

基于MPC的智能车辆换道重规划研究

为解决智能车辆换道过程中,由于其他车辆的行驶状态发生改变导致碰撞风险的问题,需对主车进行实时局部路径规划。基于模型预测控制(MPC)提出换道轨迹重规划策略,根据碰撞风险将其划分为换道轨迹修正策略、换道折返策略及前向主动避撞策略,建立基于MPC的横向控制和基于双比例积分微分控制器(PID)的纵向控制,并以纵向车速为联合点的横纵向综合轨迹跟踪控制器。将轨迹规划模块、轨迹重规划模块及轨迹跟踪模块分层集成,对换道轨迹重规划策略进行仿真验证。仿真结果表明:基于MPC的换道轨迹重规划和轨迹跟踪控制可在不同场景下实现车辆安全避撞。

基于自然驾驶数据的驾驶换道风格分析与辨识

为了研究在换道工况下个体驾驶行为及其辨识,基于NGSIM轨迹数据提取532组换道有效数据。分析处理驾驶员换道特征参数,运用K-means算法对驾驶员进行聚类分析,可分为谨慎型、一般型、激进型三类。并对聚类的参数进行统计分析和时频分析验证各类驾驶人的差异性,并提取出有效参数作为辨识模型的输入,基于惯性权重非线性变化和粒子最大速度非线性递减的策略来改进粒子群,建立起基于改进粒子群优化SVM的驾驶人风格辨识模型,对SVM模型的两个关键参数C和g进行优化更新,驾驶人识别准确率可达到96.25%,且与BP神经网络、PSO-SVM、随机森林、KNN算法相比具有更高的识别准确率。

车联网环境下应急车辆换道策略模型

为了降低应急车辆的事故到达时间,针对车联网环境下的车辆换道特点,提出一种基于博弈论思想下的应急车辆协同换道模型。分析混合交通流下应急车辆与冲突车辆的交互关系,在最小安全距离的基础上根据博弈前后的安全收益、速度收益、空间收益构建换道收益模型,对应急车辆换道进行决策引导。以应急车辆的平均速度、通行时间以及延误作为评价标准,评估模型的效用性。仿真结果表明:在同等交通密度下,博弈引导模型下应急车辆的平均速度和到达时间总体优于传统换道模型,且车辆延误通过数低于传统换道策略,在减少应急车辆到达时间的同时降低了对社会车辆的延误。

基于HMM-SVM的驾驶员换道意图辨识研究

为了提高驾驶员换道意图的辨识率,提出了一种基于隐马尔可夫模型(HMM)和支持向量机(SVM)的混合模型。通过驾驶员在环仿真实验平台采集1.2 s时间窗内的驾驶员方向盘转角、油门踏板操作信息,匹配时序性良好的各个HMM模型(紧急左换道、正常左换道、紧急右换道、正常右换道和车道保持五种HMM模型)。然后结合各个HMM模型输出的最大似然估计值,由SVM进行分类,从而辨识出驾驶员当前的换道意图。仿真结果表明:相比单独的HMM或SVM,该混合模型能够更准确地辨识驾驶员的换道意图,辨识率高达98%,且耗时仅需0.006 s,具有较好的实时性。