Pedestrian lane formation with following–overtaking model and measurement of system order

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

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.

双车道公路超车行为安全研究进展

双车道公路超车行为作为一项复杂且极为危险的驾驶行为,可能导致严重的交通事故,已成为驾驶行为研究中的热点和难点问题。聚焦双车道公路超车行为安全,运用潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)模型对该领域的潜在研究主题进行了归纳总结,梳理了超车视距评估与间隙接受决策行为建模、超车安全影响因素与碰撞风险评估、超车持续时间及交通仿真建模、超车碰撞预警系统安全分析4个主题的研究进展。结果表明:双车道公路超车行为安全研究总体上面临数据获取的难度、模型参数选择的不确定性、实际应用中的复杂性等局限性,主要体现在超车事故数据以及多车结队超车、弯道超车等非常规超车场景下的现场观测数据支撑较为薄弱;影响超车安全的道路、环境、驾驶员等因素难以被全面捕捉,增加了模型参数选择的不确定性;面对现实交通环境的复杂动态变化,超车模型的实时性、敏感性可能不足。在总结现有研究局限性的基础上,认为未来研究应从4个方面重点改进:一是完善超车视距的可靠性分析方法,拓展智能网联背景下的动态超车视距和间隙接受决策研究;二是强化多维因素对超车安全耦合影响机制的挖掘,构建超车风险的动态演化分析方法;三是深入解析异质交通流状态下超车持续时间与超车风险的关系,着力开发针对双车道公路超车的交通仿真软件;四是完善超车碰撞预警系统的微观驾驶行为和宏观交通运行影响分析,提升超车碰撞预警系统的集成应用研究。

应用Lane-Emden方程分析下月幔厚度与月核半径大小

文中取圈层结构和球对称形态为月球的基本结构假设,并以月球平均密度和无量纲惯性矩作为约束,数值求解月球Lane-Emden方程,得到下月幔厚度和月核大小的变化范围.结果表明月核的密度在4.7～7.0 g/cm^3范围内变化时,月核半径的变化范围为704～356km,相应的月幔厚度的变化范围约为33～381 km,月核占月球总质量的百分比在0.6%～7%之间变化.所得结果可为后续的关于月球内部结构的研究提供一定的参考.

“向善的治理”:理解市域社会治理的文化驱动逻辑——六尺巷“和”文化的例证

“善治”寻求公共利益最大化,是市域社会治理的目标归旨。运用嵌入性理论,遵循“文化嵌入—政治嵌入—认知嵌入”分析思路,呈现安徽省安庆市传承六尺巷“和”文化、创新市域社会治理的典型个案。研究发现,六尺巷“和”文化倡导谦和礼让,不仅可以调理社会关系,塑造社会秩序,而且对治理理念、制度、实践产生三重驱动,力促市域社会治理“向善”成为可能。这种传统文化与现代治理的有机统一,丰富了社会治理本土化的实践内涵。立足市域社会治理现代化远景目标,从政党统合、团结社会、聚焦事务、依托技术等向度思考未来进路,推动更多市域的“善治”探索迈向新高度、产生新效能。

10/100M自适应端口LANE业务的设计与实现

该文介绍了传统局域网接入ＡＴＭ网的实现方案，提出了一种具有本板交换功能的１０Ｍ／１００Ｍ自适应多端口的ＬＡＮＥ业务实现方案，并给出了软、硬件的设计实现。

基于HighD数据集的高速公路小客车换道风险分析

为探究高速公路小客车微观换道行为特性及换道风险,基于HighD数据集提取自车换道轨迹及周围车辆行驶数据,分析目标车道存在前、后车时的自车换道行为规律.以高速公路小客车换道冲突特性为基础,提取换道风险表征指标,建立基于停车距离系数的事故风险率和基于速度差的事故严重度模型,使用故障树分析法计算换道风险指数,对换道安全性进行评价,并基于k-means聚类算法将换道风险划分为4个等级;建立基于梯度提升决策树框架的换道风险预测模型,使用不同特征组合对小客车驾驶员换道风险进行预测及验证.结果表明,选取速度-加速度混合特征的换道风险预测模型预测效果最佳.研究结论可为理解高速公路小客车换道行为特性、驾驶行为模式识别以及驾驶辅助系统参数设置提供参考.

浅析基于Vxworks的LANE软件设计

文章分析了在通信协议开发中采用实时多任务操作系统进行进程规划时影响协议性能的关键因素,同时以局域网仿真(LANE)实现为例,依据实践应用结果,提出了一些高效的处理策略和方法。

开发支持LANE的ATM交换机仿真模型

通过开发支持LANE的ATM交换机仿真模型，对利用OPNET仿真工具进行二次开发作了初步探讨。分析了所开发模块的仿真模型结构和仿真运行流程，介绍了实现ATM交换机中LANE服务器功能的仿真模块的开发和整合工作，最后将所开发模块加入到ATM网络仿真模型中进行仿真验证，通过对仿真结果的分析比较，证实了开发模块的正确性和可靠性。

Visual object-oriented application for lane following on intelligent highway system

A visual object-oriented software for lane following on intelligent highway system (IHS) is proposed. According to object-oriented theory, 3 typical user services of self-check, transfer of human driving and automatic running and abnormal information input from the sensors are chosen out. In addition, the functions of real-time display, information exchanging interface, determination and operation interweaving in the 3 user services are separated into 5 object-oriented classes. Moreover, the 5 classes are organized in the visual development environment. At last, experimental result proves the validity and reliability of the control application.

相对化One-Way函数的存在性

One- Way函数在计算复杂性和密码技术中均有重要的应用 .将 Grollmann和 Selman的结果推广到相对化和非一致复杂类的情形 ,证明了复杂类 UP/ poly,UP,P/ poly等之间的包含关系与强相对化 one- way函数、弱相对化 one-

交通场景下的大型车辆右转停车检测方法研究

随着大型车辆右转必停的新交规逐步实施,针对现有的监管方式存在效率较低的问题,提出了一种大型车辆右转停车检测方法。对YOLOv7-tiny检测模型进行改进,在ELAN模块中引入注意力机制,以提升大型车辆的检测性能。设计了一种新的大型车辆右转车道定位方法,通过自适应探测车道,提高了其在不同监控视角下的定位准确性。对大型车辆的运动轨迹进行序列数据转换,并采用结合加权平均的中值滤波算法,有效降低了原始数据的噪声。在滑动窗口损失函数中引入权重因子,进一步提升了检测方法的鲁棒性。实验结果表明,改进后的大型车辆检测模型在自制数据集和公开数据集上均取得了性能提升,大型车辆右转车道定位方法具有更强的泛化能力,并且检测到的停车时间覆盖率达到了97.4%。

基于安全势场理论的高速公路大货车安全换道距离研究

针对高速公路大货车运动惯性大、受周围车辆影响明显的特点,为降低大货车换道过程中发生碰撞的风险,通过车路云协同创新的模式辅助大货车的感知与决策,本文提出了一种基于安全势场理论的大货车与周围车辆间的安全换道距离模型。首先,在分析高速公路上大货车换道的运动边界条件的基础上,分析了避免碰撞需同时满足的4种换道间距条件;其次,基于安全势场理论,分析大货车的势场空间分布,建立了其安全换道距离模型。仿真实验结果表明,随着换道加速度以及车辆间相对速度差的增加,大货车与原车道前、后车以及目标车道前车间安全距离的增长率分别减小了16.36%、23.91%和89.75%,与目标车道后车安全距离的增长率增大了28.19%。因此,高速公路上大货车安全换道时保持加速,加速度宜离开原车道时增加,进入目标车道时减小。

耦合公众车载影像与GNSS轨迹的精细道路信息众包提取方法

精细度高、现势性好的道路地图对于自动驾驶、智能交通等领域至关重要。但现有的道路测绘方法存在数据采集成本高、更新周期长等问题,无法满足智能交通对地图高时空精度的需求。公众车辆上广泛搭载的车载CCD相机与GNSS等传感器,提供了低成本、覆盖广的众包时空数据,可有效弥补传统测绘方法的不足。本文提出了一种耦合公众车载影像与GNSS轨迹的精细道路信息众包提取方法,以解决现有高精度地图成本高、更新慢、现势性低的问题。首先,本文基于跨层细化网络检测车载影像中的车道线信息;然后,基于灭点进行车载相机标定,并提出一种基于马尔可夫链的车道坐标动态计算方法,通过耦合影像与轨迹数据,解算车道线绝对位置;最后,构建一种融合众包GNSS轨迹与影像信息的高精细度道路生成模块,生成精细度高、鲜活性好、具有车道级信息的道路地图。试验以上海市虹口区的真实众包数据集为例,生成的精细道路地图在1 m范围内达到了87.43%的精度,表明本文方法有望成为一种低成本、高鲜度、覆盖广的众包测绘方法,为精细道路信息的获取提供了周期短、成本低的有效解决方案。

城市快速路互通交织区车辆的换道持续距离选择

以城市快速路互通交织区换道行为为对象,研究换道过程中换道持续距离的选择行为.以实测车行轨迹数据为基础,利用因果推断理论识别影响换道持续距离选择的主要因素:目标车辆换道前后的速度和换道持续时间、当前车道和目标车道前后车的间距.分别利用支持向量机模型和深度学习模型进行换道持续距离选择行为建模,检验换道持续距离选择行为影响因素分析的有效性.结果表明,经筛选后的影响因素提高了行为选择模型的预测速度以及深度学习模型的预测精度;支持向量模型虽然预测速度更快,但预测精度不如深度学习模型.对典型换道行为进行特征分析,为城市快速路互通交织区管理方案的制定奠定了理论基础,是对换道过程行为特征研究的有效补充,精确刻画了换道行为过程.

基于ATM技术的LANE架构

ATM作为一种现代通信技术在计算机通信网的应用,符合今后电信网和信息网的发展趋势。LANE的功能是在ATM网络上仿真LAN,LANE提供与现有MAC协议给网络层提供的驱动相同的服务接口,不需要改变该驱动,这将加速ATM的发展和应用。