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.

Approximately Bi-Similar Symbolic Model for Discretetime Interconnected Switched System

Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.

FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Evaluating the Robustness of MDSS Maintenance Forecasts Using Connected Vehicle Data

The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.

Application of Connected Truck Data to Evaluate Spatiotemporal Impact of Rest Area Closures on Ramp Parking

Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Soft-switching Grid-connected PV Inverter With Interleaved Flyback Topology

提出一种应用于单个光伏组件的软开关交错反激并网逆变器拓扑及其软开关控制策略。针对反激变压器漏感问题，提出漏感能量吸收回馈电路，实现了漏感能量吸收再利用，并实现了开关管漏源电压的钳位，提高了变换效率同时降低了开关管关断电压尖峰；提出基于数字处理器的反激变换器变开关频率谐振软开关控制策略，实现了开关管的零电压开通，同时改善了逆变器的电磁兼容特性；提出的交错并联反激逆变器有助于减小变压器和滤波器的体积，提高功率密度。详细分析变换器的工作原理，分析变开关频率谐振软开关控制方式的原理和实现条件，最后进行实验验证。

(α,β)-constraints connected dominating set algorithm in wireless sensor network

To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.

技术推动学习 为未来做好准备——美国ConnectED行动计划述评

随着对教育信息化水平的要求不断提高,美国亟待进一步提升网络连接水平。为此,奥巴马政府发起了"连结教育"(ConnectED)计划行动。该项目旨在通过升级网络、更新设备、培训教师以及推行"为未来做好准备的学区承诺"(Future Ready District Pledge)来提高美国教育信息化水平和质量。我国教育信息化发展可以从该项目的实施中吸取经验。

DYNAMIC ANALYSIS OF DAMPER CONNECTED ADJACENT BUILDINGS UNDER EARTHQUAKE

A formulation of the multi degree of freedom equations of motion for damper connected adjacent multi story buildings under earthquake excitation is presented.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequency domain is implemented in a computer program to handle the non orthogonal damping properties of the system.The effectiveness of joint dampers is then investigated in terms of the reduction of displacement,acceleration and shear force responses of adjacent buildings.

Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment

We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from con-nected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various pene-tration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetra-tion rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous pre-diction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associ-ated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.

Dynamic responses of two buildings connected by viscoelastic dampers under bidirectional earthquake excitations

In this study,dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations are extensively investigated.The two buildings are a 10-story building and a 16-story building,with the shorter building on the left.Viscoelastic dampers are installed at all fl oors of the shorter building.Equations of motion are formulated using a fractional derivative model to represent the viscoelastic dampers.Three cases are considered with mass eccentricities at 0,10% and-10% with respect to the dimensions of the buildings.The responses of the buildings are numerically predicted at different damper properties.The simulation results indicated that the maximum horizontal responses of the buildings without eccentricities are signifi cantly mitigated.However,torsional effects are adversely increased.For asymmetric buildings,the effectiveness of the connecting dampers is affected by building eccentricities.As a result,mass eccentricities must be taken into account in damper selection.When compared with vibrations induced by unidirectional excitations,bidirectional excitations can increase the responses of coupled asymmetric buildings.In addition,installing dampers only at the top fl oor of the shorter building may cause a sudden change in lateral stiffness of the taller building.Consequently,the story shear envelopes of the taller building are changed.

改进RRT-Connect与DWA算法的巡检机器人路径规划研究

针对传统RRT-Connect算法在密集复杂环境中路径规划效率低、动态避障效果差等问题,提出一种改进RRT-Connect与DWA融合算法。该算法通过改进采样策略、动态步长优化和碰撞检测引导随机树生长;在随机树中采用贪心策略和角度约束优化路径。基于巡检机器人建立运动学模型,通过速度采样空间生成轨迹簇;建立模糊逻辑系统自适应调整DWA算法评价函数的权重系数,将全局最优路径点融入DWA算法中实现全局最优路径和实时避障。仿真结果表明,在油气站场密集复杂环境中,改进RRT-Connect算法较传统算法路径缩短约27.09%,平滑度提高约84.6%,碰撞距离提高约18.75%;改进融合算法路径减少约2.97%,平滑度提高约78.8%,碰撞距离提高约30.6%,验证了提出算法的有效性。

Elucidation of the ‘Honeycrisp’ pedigree through haplotype analysis with a multi-family integrated SNP linkage map and a large apple (Malus×domestica) pedigree-connected SNP data set

The apple(Malus×domestica)cultivar Honeycrisp has become important economically and as a breeding parent.An earlier study with SSR markers indicated the original recorded pedigree of‘Honeycrisp’was incorrect and‘Keepsake’was identified as one putative parent,the other being unknown.The objective of this study was to verify‘Keepsake’as a parent and identify and genetically describe the unknown parent and its grandparents.A multi-family based dense and high-quality integrated SNP map was created using the apple 8 K Illumina Infinium SNP array.This map was used alongside a large pedigree-connected data set from the RosBREED project to build extended SNP haplotypes and to identify pedigree relationships.‘Keepsake’was verified as one parent of‘Honeycrisp’and‘Duchess of Oldenburg’and‘Golden Delicious’were identified as grandparents through the unknown parent.Following this finding,siblings of‘Honeycrisp’were identified using the SNP data.Breeding records from several of these siblings suggested that the previously unreported parent is a University of Minnesota selection,MN1627.This selection is no longer available,but now is genetically described through imputed SNP haplotypes.We also present the mosaic grandparental composition of‘Honeycrisp’for each of its 17 chromosome pairs.This new pedigree and genetic information will be useful in future pedigree-based genetic studies to connect‘Honeycrisp’with other cultivars used widely in apple breeding programs.The created SNP linkage map will benefit future research using the data from the Illumina apple 8 and 20 K and Affymetrix 480 K SNP arrays.

Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections:A Review

Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.

Pedestrian Facilities Capacity and Level of Service at Intersections in a Connected and Autonomous Vehicle Environment

It is inevitable that Connected and Autonomous Vehicles (CAVs) will be a major focus of transportation and the automotive industry with increased use in future traffic system analysis. Numerous studies have focused on the evaluation and potential development of CAVs technology;however, pedestrians and bicyclists, as two essential and important modes of the road users have seen little to no coverage. In response to the need for analyzing the impact of CAVs on non-motorized transportation, this paper develops a new model for the evaluation of the Level of Service (LOS) for pedestrians in a CAVs environment based on the Highway Capacity Manual (HCM). The HCM provides a methodology to assess the level of service for pedestrians and bicyclists on various types of intersections in urban areas. Five scenarios were created for simulation via VISSIM (a software) that corresponds to the different proportions of the CAVs and different signal systems in a typical traffic environment. Alternatively, the Surrogate Safety Assessment Model (SSAM) was selected for analyzing the safety performance of the five scenarios. Through computing and analyzing the results of simulation and SSAM, the latter portion of this paper focuses on the development of a new model for evaluating pedestrian LOS in urban areas which are based upon HCM standards which are suitable for CAVs environments. The results of this study are intended to inform the future efforts of engineers and/or policymakers and to provide them with a tool to conduct a comparison of capacity and LOS related to the impact of CAVs on pedestrians during the process of a transportation system transition to CAVs.

Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.

Interleaved-Connected Split Planar Resonant Inductor Design in 1 kV SiC LLC Converters

Design method of split planar resonant inductor in 1 kV SiC logical link control(LLC)converter is proposed,which ensures the converter power density of 93.59 W/in^3 and peak efficiency of 95.73%.Split resonant inductor helps to provide symmetrical resonant current by symmetrical impedance,and improves the distortion of resonant current,which ensures the efficiency of the whole converter.An interleaved winding connecting scheme improves the power density of the planar magnets,which contributes to power density improvement.Design method and calculation process of such split planar resonant inductor are provided.To verify the feasibility of the proposed design method,a 1 kV/48 V 6.6 kW,210 k Hz SiC LLC prototype was built,and the experimental results are given.