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.

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.

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

Enhancing Urban Mobility: Exploring the Potential of Exclusive Motorcycle Lane Using VISSIM

The proliferation of Mobility on Demand (MOD) services has ushered in a surge of ridesharing platforms, catalyzing the emergence of micro mobility solutions like motorcycle sharing. Consequently, motorcycles have witnessed unprecedented growth over recent decades. This proliferation, while offering convenience, has introduced challenges such as diminished road capacity, and compromised safety. This study advocates for the implementation of exclusive motorcycle lanes to mitigate the ensuing disorderliness using VISSIM microsimulation platform. Empirical data from a key corridor in Dhaka is harnessed to calibrate and simulate network performance scenarios—pre- and post-implementation of dedicated motorcycle lanes. The outcomes of our simulation experiments exhibit the implementation of dedicated motorcycle lanes leads to a reduction in vehicular throughput but improvement the flow of motorcycles. In addition, Surrogate Safety Measures (SSMs) demonstrate the safety improvements through implementation of the treatment.

Lane Line Detection Based on Improved PINet

Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines;images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines;and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.

Review and Analysis: Fate of Arsenic Applied to Canal Shipping Lane Vegetation and United States Military Base Grounds in the Panama Canal Zone

The opening of the Panama Canal in 1913 increased the availability of internationally traded goods and transformed ocean-shipping by shortening travel time between the Atlantic Ocean and Pacific Ocean. The canal sparked the growth of port authorities and increased ship tonnage on both coasts of Panama. Since the construction of the Panama Canal, in the 1910s, pesticides, herbicides and chemicals, including arsenic, have been essential for controlling wetland vegetation, including hyacinth, which blocked rivers, lakes, and the canal as well as managing mosquitoes. Pesticides and chemicals flowed into Lake Gatun (reservoir) either attached to sediment or in solution during the monsoon season. Lake Gatun was the drinking water source for most of the people living in the Panama Canal Zone. The United States military base commanders had the ability to order and use cacodylic acid (arsenic based) from the Naval Depot Supply Federal and Stock Catalog and the later Federal Supply Catalog on the military base grounds in the Panama Canal Zone. Cacodylic acid was shipped to Panama Canal Zone ports, including Balboa and Cristobal, and distributed to the military bases by rail or truck. The objective of this study is to determine the fate of arsenic: 1) applied between 1914 and 1935 to Panama Canal shipping lane hyacinth and other wetland vegetation and 2) cacodylic acid (arsenic) sprayed from 1948 to 1999 on the US military base grounds in the Panama Canal Zone.

RAIENet:End-to-End Multitasking Road All Information Extractor

Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detection accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird’s eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at https://github.com/mayberpf/RAIEnet.

Design Strategy of Collector-Distributor Lanes in Urban Interchanges

Based on the functions and characteristics of the interchange collector-distributor lanes,with Harbin as an example,it is proposed in this paper that the local characteristics and traffic flow characteristics should be considered in the design of the interchange collector-distributor lanes,which includes the analysis of function,location,and many other aspects,in hopes to provide reference for the design of collector-distributor lanes in other regions of our country.

GDMNet: A Unified Multi-Task Network for Panoptic Driving Perception

To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object detection.Firstly,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient flow.Secondly,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training adjustments.Experimental results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,respectively.The detection performance surpasses that of other single-task or multi-task algorithm models.

A Review of Lane Detection Based on Deep Learning Methods

Lane detection is animportant aspect of autonomous driving,aiming to ensure that vehicles accurately understand road structures as well as improve their ability to drive in complex traffic environments.In recent years,lane detection tasks based on deep learning methods have made significant progress in detection accuracy.In this paper,we provide a comprehensive review of deep learning-based lane detection tasks in recent years.First,we introduce the background of the lane detection task,including lane detection,the lane datasets and the factors affecting lane detection.Second,we review the traditional and deep learning methods for lane detection,and analyze their features in detail while classifying the different methods.In the deep learning methods classification section,we explore five main categories,including segmentation-based,object detection,parametric curves,end-to-end,and keypoint-based methods.Then,some typical models are briefly compared and analyzed.Finally,in this paper,based on the comprehensive consideration of current lane detection methods,we put forward the current problems still faced,such as model generalization and computational cost.At the same time,possible future research directions are given for extreme scenarios,model generalization and other issues.

基于Bounding Box的车辆行为标注

针对目前自动驾驶汽车行为预测对目标行为标注准确率低及压线行驶行为无法标注问题,首先通过建立车辆包围盒(Bounding Box)将车辆的形状描述出来,随后通过Bounding Box 4个顶点与车道线的相交关系来判断目标车是否有换道行为。对于无车道线的情况,首先通过自车轨迹左右拓展出虚拟车道线,通过Bounding Box与虚拟车道线的关系判断目标车是否有切入自车道的行为,随后进行标注。通过Bounding Box对目标行为进行标注可以将标注的准确率提高至80%,并且可以将压线行驶行为标注出来,为后续行为预测模型训练打下良好的基础。

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.

基于Kriging遗传算法的高速公路应急车道管控优化

针对如何在不同交通流状况下有效提高高速公路运行效率和降低安全风险的问题,提出基于Kriging代理模型的遗传算法优化应急车道管控策略。结合应急车道开放策略的时空特性设计数学优化模型,通过引入Kriging代理模型,结合遗传算法搭建优化框架,采用仿真软件获取数据训练代理模型,以此求解带有开放时间和开放空间双重约束的总行程时间与总碰撞暴露时间最小化问题。对车道控制时间与空间变量的变化频次进行了约束,并对目标函数中效率与安全指标权重变化对优化结果的影响进行了分析。实验表明:该优化方法使路网总行程时间减小14.9%,碰撞暴露时间减小44.2%,控制效果提升。

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.

基于PINet+RESA网络的车道线检测算法

实例点网络(point instance network, PINet)在物体遮挡、光照变化和阴影干扰等场景中检测准确性高,但实时性表现不佳.在保证PINet模型精度的前提下,为提升网络的推理速度,提出一种结合循环特征移位聚合器(recurrent feature-shift aggregator, RESA)算法的车道线检测模型.通过算力分析,只采用1个瓶颈网络(bottle-neck)作为预测网络(predicting network),目的是为了去除冗余的多尺度操作,以加快模型的推理速度.为了弥补模块剪枝造成的精度下降,引入了RESA模块以捕获图像中跨行、列的空间信息,增强骨干网络提取到的车道线特征.将改进后的模型在Tusimple、CULane、Custom数据集上进行测试.结果表明:改进后的网络模型在物体遮挡、光照变化、阴影干扰等多种复杂场景下表现突出,对车道分割准确率、实时处理速度有大幅改善,检测识别效果优于传统PINet网络算法,除F1指标提升较小外,推理速度在3个数据集下分别提升20.3%、52.9%及13.9%.

Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

Lane Recognition Algorithm Using the Hough Transform Based on Complicated Conditions

At present, most lane line detection methods are aimed at simple road surface. There is still no good solution for the situation that the lane line contains arrow, text and other signs. The edge left by markers such as arrow and text will interfere with the detection of lane lines. In view of the situation of arrow mark and text mark interference between lane lines, the paper proposes a new processing algorithm. The algorithm consists of four parts, Gaussian blur, image graying processing, DLD-threshold (Dark-Light-Dark-threshold) algorithm, correlation filter edge extraction and Hough transform. Among them, the DLD-threshold algorithm and related filters are mainly used to remove the identification interference between lane lines. The test results on the Caltech Lanes dataset are given at the end of the article. The result of verification of this algorithm showed a max recognition rate of 97.2%.