Different Foliar Fertilizer Treatments Affect the Yield and Quality of Brassica napus L.for Both Vegetable and Oil Dual-Use Rape

Field experiments were carried out to study the effect of different foliar fertilizer treatments on the agronomic traits,yield,and quality of Brassica napus L.for both vegetable and oil dual-use rape.The seedlings of‘Youtai 929’,‘Jinyoutai 1’,and‘Zhongshuang 11’were sprayed with potassium dihydrogen phosphate,naphthylacetic acid,and selenium fertilizer,and the agronomic traits,yield,and quality were determined.The results showed that the naphthylacetic acid treatment had the highest indexes before winter,followed by the selenium fertilizer treatment.At the harvesting stage,the plant height,number of siliquae,yield per plant,and number of effective branches on the main stem of the three varieties were the highest in the naphthylacetic acid treatment,followed by those in the potassium dihydrogen phosphate treatment.The fresh shoot quality was the best in the selenium fertilizer treatment and the second in the naphthylacetic acid treatment.‘Jinyoutai 1’applied with naphthylacetic acid had the highest economic benefit of 4971.78 CNY/667m2,which was 642.32 CNY/667m2 higher than that of the control.The findings suggested that naphthylacetic acid was suitable to be used as the foliar fertilizer for the seedlings of B.napus varieties for both vegetable and oilseed,and the additional application of selenium fertilizer demonstrated better effect.

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.

Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation

This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication(DFRC)systems.Focusing on the standardized linear frequency modulation(LFM)signal by additional phase,a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics,are generated.Then,these signals map the different information as well as their phases are also modulated to increase the communication bit rate,thus yielding a series of dual-use signals.Finally,the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification,confirming the effectiveness of the designed signals compared with the existing design strategy.

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.

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。

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。

Lane-Emden型方程的广义Vieta-Fibonacci多项式迭代方法

基于广义Vieta-Fibonacci多项式的拟线性化矩阵配置方法,提出了一种求带有Dirichlet边界条件、Neumann边界条件和Neumann-Robin边界条件的一类Lane-Emden型微分方程的数值解的方法 .首先将Lane-Emden型方程拟线性化,然后利用广义Vieta-Fibonacci多项式展开得到矩阵形式,再用迭代方法进行求解.最后通过求不同边值条件下的Lane-Emden型方程的近似解,将数值结果与其他方法得到的近似解进行对比,验证了广义Vieta-Fibonacci多项式拟线性化迭代方法的有效性和准确性.

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。

高速公路收费站ETC/MTC混合收费车道通行能力计算模型研究

分析了ETC/MTC混合收费车道入口ETC车辆占比αE和连续到达ETC车辆排队长度w对混合收费车道平均服务时间tS的影响;对SHAKER模型进行了修正,并对修正SHAKER模型参数进行了标定,建立VISSIM仿真模型对修正SHAKER模型的有效性进行了验证;分析了ETC/MTC混合收费车道通行能力与ETC车辆占比之间的关系,提出了考虑车辆平均服务时间的ETC/MTC混合收费车道入口通行能力估计模型,并与余弦函数模型进行了对比分析。结果表明:不同ETC车辆占比下,ETC/MTC混合收费车道入口通行能力修正SHAKER模型计算值和VISSIM模型仿真值的相对误差ε<10%,在允许范围之内,验证了修正SHAKER模型的有效性;ETC/MTC混合收费车道通行能力随着驶入混合车道ETC车辆占比的增大而增大;与余弦函数模型相比,笔者提出的估计模型更能反映ETC/MTC混合收费车道的交通运行特性。

视觉融合下的复杂路况车辆换道决策模型

车辆换道是一个融合车辆行为及其周围环境的多因素动态过程,不合理的换道行为会招致严重的交通事故.现有安全防撞模型未考虑换道时多车辆间的耦合关系,且换道车辆的感知方式有一定局限性,在需强制性换道时决策效果不理想.因此本文提出视觉融合下的复杂路况车辆换道决策模型.该模型以单视角补偿双目视差计算的视觉融合方法感知参与换道的多车辆,解决了因车辆遮挡覆盖等非线性运动导致的多目标车辆轨迹预测误差问题,通过提取出三维的换道行为参数输入RBF神经网络进行可行性安全评估,可使车辆在合适时机进行换道.实验结果表明,提出的车辆换道决策模型在相同实验环境下相较单目检测算法YOLOv3准确性可提升5.1%,相较选取的基准双目检测算法准确性提升0.7%,且对换道进行安全评估的预测综合准确率达97.33%,能满足自动驾驶车辆强制性换道需求.