Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.

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.

Cooperative Lane-Change Maneuver for Multiple Automated Vehicles on a Highway

With the development of vehicle-to-vehicle(V2V)communication,it is possible to share information among multiple vehicles.However,the existing research on automated lane changes concentrates only on the single-vehicle lane change with self-detective information.Cooperative lane changes are still a new area with more complicated scenarios and can improve safety and lane-change efficiency.Therefore,a multi-vehicle cooperative automated lane-change maneuver based on V2V communication for scenarios of eight vehicles on three lanes was proposed.In these scenarios,same-direction and intersectant-direction cooperative lane changes were defined.The vehicle that made the cooperative decision obtained the information of surrounding vehicles that were used to cooperatively plan the trajectories,which was called cooperative trajectory planning.The cooperative safety spacing model was proposed to guarantee and improve the safety of all vehicles,and it essentially developed constraints for the trajectory-planning task.Trajectory planning was treated as an optimization problem with the objective of maximizing safety,comfort,and lane-change efficiency under the constraints of vehicle dynamics and the aforementioned safety spacing model.Trajectory tracking based on a model predictive control method was designed to minimize tracking errors and control increments.Finally,to verify the validity of the proposed maneuver,an integrated simulation platform combining MATLAB/Simulink with CarSim was established.Moreover,a hardware-in-the-loop test bench was performed for further verification.The results indicated that the proposed multi-vehicle cooperative automated lane-change maneuver can achieve lane changes of multiple vehicles and increase lane-change efficiency while guaranteeing safety and comfort.

Hybrid cooperative intersection management for connected automated vehicles and pedestrians

Connected Automated Vehicles(CAVs)have drawn much attention in recent years.High reliable automatic technologies can help CAVs to follow given trajectories well.However,safety and efficiency are hard to be ensured since the interactions between CAVs and pedestrians are complex problems.Thus,this study focuses on cooperative intersection management for CAVs and pedestrians.To avoid the effects of uncertainty about pedestrian behaviors,an indirect way is to use pedestrians’signal lights to guide the movements of pedestrians,and such lights with communication devices can share information with CAVs to make decisions together.In time domains,a general conflict-free rule is established depending on the positions of CAVs and crosswalks.Geometric analysis with coordinate calculation is used to accurately determine the feasible vehicle trajectories and the reasonable periods for signal lights turning green.Four control strategies for the same conditions are compared in simulation experiments,and their performances are analyzed.We demonstrate that the proposed cooperative strategy not only balances the benefits of vehicles and pedestrians but also improves the traffic efficiency at the intersection.

On the impact of connected automated vehicles in freeway work zones:a cooperative cellular automata model based approach

Purpose–Freeway work zones have been traffic bottlenecks that lead to a series of problems,including long travel time,high-speed variation,driver’s dissatisfaction and traffic congestion.This research aims to develop a collaborative component of connected and automated vehicles(CAVs)to alleviate negative effects caused by work zones.Design/methodology/approach–The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations.Findings–Simulation results show that,with the proposed component and penetration of CAVs,the average performances(travel time,safety and emission)can all be improved and the stochasticity of performances will be minimized too.Originality/value–To the best of the authors’knowledge,this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.

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.

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.

自主泊车轨迹规划技术发展现状综述

自主代客泊车系统是集智能驾驶环境感知、决策规划、运动控制技术于一体的综合系统,轨迹规划直接关系到代客泊车停车过程的效率、能耗、安全性和舒适性。为梳理自主泊车轨迹规划技术的发展现状,回顾了泊车技术的发展历程,针对泊车过程中单车轨迹规划问题开展调研,分析了面向自主代客泊车轨迹规划研究的进展。鉴于从单车智能到多车协同的转变展现了更大的系统优化潜力,继而梳理了多车协同轨迹规划的基本方法和研究现状,探讨了泊车场景下的协同规划研究进展及特点。对自主代客泊车轨迹规划的现存问题与未来发展趋势进行了分析和展望。

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenarios(i.e.,uniform vehicle arrivals,lengths,and speeds)have been proposed.However,theoretical analyses and comparisons of these approaches are lacking.In this study,we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance.We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.

基于场端智能的自动泊车系统安全即服务架构研究

基于场端智能的自动泊车系统是一种重要的车路协同应用案例。针对此类系统当前行业痛点,文章创新性地提出了场端基础设施安全即服务(SafetyasaService)架构,通过将场端功能模块化、服务化,显著降低了车端适配难度,扩大了智能基础设施适用范围,有助于推动智能驾驶从L2向L4及以上平滑演进,加速产业化落地。

基于区块链的配电网能效自动化管理方法

随着可再生能源的发展和分布式电源的推广,电网需求侧能效管理工作将逐步由集中式优化调度计算转向分布式计算。物联网技术和区块链技术的进步将为实现能效管理系统的分布式系统提供技术基础。文章提出了一种基于物联网智能电表和微电网的多用户分布式能效管理系统,并通过提出的非合作博弈论模型对家用电设备进行调度计算,成功实现了降低用户家庭电费支出和整个社区微电网能耗总成本的目标。用户可在家庭的分布式能源、社区微电网以及公用电网三种中择优选择购电,并在博弈中优化自己的日常家电使用策略,使得市场所有用户都可将能源消耗成本降至最低,实现纳什平衡。在此基础上,该模型运用区块链技术为参与者之间提供可靠的、安全的、私密的通信媒介,实现用户家电设备的智能监控,通过智能合约对用电量进行计费。算例分析结果表明,该方法能最大限度地降低能耗总成本和各用户能耗成本。

电气自动化专业实践能力的教学研究与探索

现代工业和科技发展迅速,对电气自动化专业人才的实际操作能力要求越来越高。通过实践能力教学,学生可以更好地适应行业需求,对于学生的职业发展至关重要。该文通过分析现有教学模式的优缺点,提出了改进措施,应该加强校企合作,建立实训基地,同时引入先进的实验设备与软件工具,优化课程体系,增加实践课比例,此外培养教师实践能力,提升教学质量。通过这些措施,旨在提高学生的实际操作技能和创新能力,增强他们在复杂电气自动化系统中的应用和解决问题的能力,为社会输送高素质的电气自动化专业人才。

继电保护与配电自动化协同作用的配电网故障处理研究

配电网故障处理是保证电力系统安全运行的重要环节。继电保护作为一种重要的电力系统保护手段,能够及时准确地判断电力系统的故障类型和位置,并采取相应的措施进行处理。配电自动化技术能够实现对配电网的监控、控制和管理,提高故障检测和处理的速度和精度。继电保护与配电自动化的配合能够有效提高配电网故障处理的效率和可靠性,同时也便于对故障进行分析和评估。文章对继电保护与配电自动化协同作用的配电网故障处理进行分析。

城市能源互联网功能体系及应用方案设计

北京延庆地区拥有全国最大的微电网群和丰富的可再生能源。依托北京延庆智能电网创新示范区建设工程,分析了延庆地区的新能源基础条件和特征,从能源供给/消费形式、承载能源输送的主要载体、管理决策与公共服务等几个方面阐述了示范区建设内容,构建了能源互联网的应用框架模型,详细阐述了支持广域潮流流动的交直流主动配电网、分层分区的分布式智能调控管理和开放的公共服务模式,探讨了未来的管理和商业模式的探索方法和途径。

基于主动配电网的城市能源互联网体系架构及其关键技术

从能源供给和能源消费、能源输送载体、多源大数据以及协同优化调控等几个方面详细阐述城市能源互联网的应用框架模型,深入分析基于主动配电网的能源生产消费一体化、多源协同优化调度和分布式分层智能调控管理,探讨未来城市能源互联网的运营模式。针对未来城市能源互联网的建设内容、建设模式和前景规划设计城市能源互联网的应用场景,并根据实际能源互联网示范介绍当前的能源互联网发展现状。

遥视与EMS运行的协同性研究

无人值班变电站新增遥视系统后,为电力系统的安全运行进一步提供了保证。文中在分析遥视与EMS协同性的基础上,提出利用广义黑板模型作为遥视与EMS协同的基本模型,将遥视和EMS各分为若干个主体,各主体之间采用广义黑板共享信息。同时,还介绍了广义黑板的基本原理和实现细节,以及遥视与EMS协同工作后EMS和遥视系统的各相关主体在功能和设计上的变化。该方法不仅适用于当前遥视系统与EMS的互联,而且也适用于未来实现多媒体EMS。

基于车车通信的车辆并行协同自动换道控制

为研究多车协同自动换道,分析了三车道八车场景中两车协同行为,对两车并行协同行为进行界定,提出了两车并行协同自动换道控制策略。针对三车道八车场景,以车车通信为条件,实时获取周围车辆的运动状态变化和换道意图,根据周围车辆的运动状态,设计了两车协同换道轨迹模型,规划换道车辆的参考协同换道轨迹。在此基础上,提出了并行协同自动换道安全距离模型,以形成换道车辆与周围车辆的换道轨迹约束,保证生成安全可靠的协同换道轨迹。最终根据规划的安全参考轨迹,采用模型预测控制算法,实时优化换道车辆的速度和前轮转角,实现轨迹纵横向跟踪。仿真结果表明,所提出的并行协同控制策略能够实现两车安全协同自动换道,同时提高换道效率。

无人驾驶汽车协同感知信息传输负载优化技术

无人驾驶近年来成为了学术界和工业界的研究热点,无人驾驶汽车的环境感知则是其中的重要基础.仅通过提升无人驾驶汽车上的传感器数量和精度并不能完全消除车辆的感知盲区,因此无人驾驶汽车与路边基础设施进行协同环境感知越来越受到关注.通过车路协同感知,无人驾驶汽车的感知范围能够得到有效扩展,有助于消除感知盲区,对于提升无人驾驶的安全性具有重要意义.在各类环境感知信息中,摄像头拍摄的视频占有最重要的地位.然而,视频帧所包含的数据量较大,传输每个视频帧会导致网络负载过重,传输延迟增大,影响环境感知信息的时效性.本文提出了一种视频感知数据的传输负载优化方法,主要思想是通过路边基础设施把视频帧中的静态背景与动态前景进行分离,仅在初始时传输一次静态背景,其余每次仅传输动态前景信息,这样可以使得传输负载大幅降低.无人驾驶汽车将收到的静态背景图像与动态前景图像重新融合成视频帧,然后基于视频帧所反映的行车环境做出正确的驾驶决策.对于静态背景与动态前景的分离,本文提出了一种基于像素值计算的视频帧背景去除和降噪方法,能够快速地从视频帧中提取动态前景;对于静态背景与动态前景的融合,本文提出了一种基于生成对抗网络的视频帧生成方法,能够快速地把静态背景和动态前景融合成视频帧.通过在真实数据集上的测试可知,本文提出的方法能够在重要环境感知信息不丢失的前提下使传输负载降低85%以上,感知信息处理时间降低70%以上.这表明本文提出的方法能够高效地实现无人驾驶汽车与路边基础设施的协同环境感知,有助于构建更加安全的无人驾驶系统.