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.

利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法

随着智慧交通和精细化导航技术的迅速发展,人们对道路地图要素的覆盖度、精准度、丰富度与新鲜度需求越来越高,而U-Turn作为城市路网连通关系的重要因素,成为道路地图更新的重要内容之一。现有专业道路测绘模式对U-Turn数据存在采集成本高、更新周期长、数据处理繁等问题,导致U-Turn数据无法满足智慧交通导航需求。本文采用车载GNSS轨迹大数据,提出了一种U-Turn道路结构信息获取方法。首先通过轨迹跟踪提取车辆掉头点对与行为;然后利用DBSCAN聚类算法提取U-Turn掉头类簇;再依据U-Turn流量占比等特征构建支持向量机二分模型,自适应剔除违规掉头类簇,并区分出U-Turn结构有无通行时间限制;最后根据掉头类簇在道路结构中的分布特征,识别U-Turn位置与空间结构。试验以武汉市滴滴网约车GNSS轨迹,对江汉区183个路段采用本文方法进行探测,U-Turn结构信息识别召回率为88.3%,精确率为87.6%,位置识别的横向和纵向精度分别为3.40 m和5.90 m,试验结果表明本文方法可以有效地从车载GNSS轨迹数据中获取U-Turn的位置与结构类型,为U-Turn数据获取提供了周期短、成本低的有效解决方案。

Development of Vehicle Lane Changing Model in Approaching the U-Turn Facility Road Segment

Accidents are in rising mode and became the main problem all over the world especially in Malaysia as the reasons including the condition of the road, driver＇s reaction and the road environment. Actually, those condition also factors to execute the lane changing which experienced by all drivers such as in U-turn road segment. In approaching U-turn segment, drivers needed to make a decision whenever any disruption in front of them such as merging vehicle because they have their own perspective and desire. For that purpose, this research is focusing on the reaction of the driver in approaching the U-turn facility road segment especially in speed （V）, reaction time （RT） and distance where from those parameters and their relationships, the statistical model was developed and used in estimating the safe distances to execute the lane changing from the merging vehicle. The data were taken from the field and driving simulator to come out with the raw data. The field data were from video recording that has been used to simulate the driving simulator. Therefore, through the relationship between the RT, speed （V） and distance of the subject vehicle to the merging vehicle, the statistical model has been developed with the equation D.4MVUT = （13.448 ＋ 1.410 RT- 0.075 V）.

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.

A Novel Ego Lanes Detection Method for Autonomous Vehicles

Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

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.

网联混合流高速公路车道管理及通行能力分析

为提高网联混合交通流环境下高速公路基本路段通行能力,需针对车流量、网联自动驾驶车辆(CAV)比例制定与之匹配的车道管理策略。针对低CAV比例环境下CAV专用车道闲置浪费的问题,提出了一种CAV优先车道管理策略。利用跟驰模型推导了混合交通流基本图模型,构建了人工驾驶车辆(HV)专用车道、CAV专用车道、混行车道及CAV优先车道通行能力模型,分析了高速公路不同车道组合方案适用的交通需求范围。然后,以双向4车道高速公路为例,基于SUMO仿真平台分析了CAV优先车道性能,对不同车道组合方案的通行能力和适用范围进行了仿真验证,并从效率、安全及能耗角度对车道组合方案进行了比较。结果表明:在适用性方面,CAV比例p≤0.5时,两条混行车道适用的交通需求范围最大,CAV优先车道次之,CAV专用车道最小;在效率方面,p≤0.5时,设置CAV专用车道方案的效率最差,CAV优先车道表现较好,且p越小优先车道效率提升越显著;在安全、能耗方面,设置CAV专用车道方案的能耗和安全性能较佳,CAV优先车道次之;在p>0.5时,设置CAV专用车道方案的综合表现更优;在多种车道管理方案均可行时,相比于两条混行车道方案,采取其他车道管理措施在安全、能耗上的表现均有提升;在需求D≤2500 veh/h,p≤0.5时,相比于其他车道方案,采用CAV优先车道方案既能满足通行需求、降低能耗,也能更充分地利用道路资源,综合表现均衡。

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。

基于驾驶场景与决策规则的智能汽车换道决策

复杂交通环境中,换道决策直接影响智能汽车自主换道效果,然而在换道决策过程中依旧存在着预测正确率低以及决策安全的问题。因此,针对这一问题,提出了基于驾驶场景和决策规则的换道决策模型。考虑换道后的交通行驶状况对换道决策的影响,引入换道后的期望速度和换道前后与前车的距离作为新的特征变量,基于特征变量与换道决策的相关性建立了换道决策规则。建立了模拟真实驾驶环境的换道场景数据集,扩充了NGSIM换道场景数据集,并对其进行了有效性验证。针对换道决策的多参数和非线性问题,提出了基于贝叶斯优化核函数的支持向量机模型,在换道场景数据集上进行测试验证。结果表明:新引入的决策特征变量对换道行为有积极作用,换道场景数据集能够模拟真实的换道场景,可进一步应用到换道决策和轨迹规划的研究中,支持向量机模型对换道行为的预测正确率达95.40%,高于其他机器学习分类器,提高了换道行为的安全性。

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.

基于时域卷积网络与注意力机制的车辆换道轨迹预测模型

精准的车辆轨迹预测模型可以为自动驾驶车辆提供其周围车辆的准确运动状态信息,进而判断本车与周围车辆短期内是否有发生冲突的可能性。本文提出一种基于时域卷积网络与注意力机制(Temporal Convolutional Networks with Attention mechanism,TCN-Attention)的车辆换道轨迹预测模型。该模型以时域卷积网络作为当前输入的特征提取器,利用时间与空间注意力机制使模型在不同时间和空间位置之间建立动态关联,更准确地捕捉车辆之间的动态时空相关性,实现准确预测车辆换道轨迹。与传统单一车辆轨迹特征输入不同,本文通过对输入特征进行多维扩充与融合,进一步提高了轨迹预测准确率。此外,本文提出一种换道执行起止时刻定义方法更准确地确定数据集中的换道起止时刻。实验表明,本文所提模型能以高准确率预测变换车道轨迹,在整体效果上优于其他深度学习模型,与ConvLSTM (Convolution Long Short-Term Memory)相比,TCN-Attention的平均绝对误差(Mean Absolute Error,E_(MAE))降低了69.8%,均方根误差(Root Mean Square Error,E_(RMSE))降低了49.15%,平均绝对百分比误差(Mean Absolute Percentage Error,E_(MAPE))降低了14.24%。

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.

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.

基于逆透视变换的车道线激光精准检测方法

车道线检测作为智能汽车安全驾驶的主要研究方向,能够在汽车偏离车道时及时发出预警,有效缓解交通拥挤、安全问题,但常规方法易受光照强度、阴影等环境因素影响,限制其使用范围,且检测误差较大。为此,提出基于逆透视变换的车道线激光精准检测方法。该方法选用RS-LiDAR-16激光雷达作为车道数据采集装置,借助逆透视变换、顶视图空间坐标系转换各激光点数据,利用最大类间与最小类间方差算法找出激光点反射强度最佳阈值,作为车道表面及车道线数据判断依据,通过二值化算得出车道线各点数据,凭借最小二乘拟合法将这些数据拟合成线,最终检测出车道线。实验结果表明,所提方法车道线检测精准度高,逆透视变换降低了环境对检测结果的干扰。