The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of survivability.In this paper,we propose a model for survivability qua...The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of survivability.In this paper,we propose a model for survivability quantification,which is acceptable for networks carrying complex traffic flows.Complex network traffic is considered as general multi-rate,heterogeneous traffic,where the individual bandwidth demands may aggregate in complex,nonlinear ways.Blocking probability is the chosen measure for survivability analysis.We study an arbitrary topology and some other known topologies for the network.Independent and dependent failure scenarios as well as deterministic and random traffic models are investigated.Finally,we provide survivability evaluation results for different network configurations.The results show that by using about 50%of the link capacity in networks with a relatively high number of links,the blocking probability remains near zero in the case of a limited number of failures.展开更多
The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to hazards.A...The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To efficiently search for critical scenarios with the trigger of complex traffic flow,this scenario is encoded as genes and it is regenerated through selection,mutation,and crossover iteration processes,known as the Genetic Algorithm(GA).Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios,contributing to the construction of the SOTIF scenario library.展开更多
Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliabl...Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.展开更多
Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the opera...Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.展开更多
Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air tr...Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.展开更多
It is an important issue to assess traffic situation complexity for air traffic management.There is a lack of systematic review of the existing air traffic complexity assessment methods,and there is no consideration o...It is an important issue to assess traffic situation complexity for air traffic management.There is a lack of systematic review of the existing air traffic complexity assessment methods,and there is no consideration of the role of airspace and traffic coordination mechanism.A new 3-D airspace complexity measurement method is proposed based on route structure constraints to evaluate the air traffic complexity objectively.Firstly,the model of the impact on horizontal and vertical direction for“aircraft pair”is established based on the route guidance.After that,the coupled complexity model for 3-D airspace is given according to the modification on the model in terms of flight standardization.Finally,the global model of the airspace traffic complexity is established.It is proved by the experimental data from the actual operation in airspace that the proposed model can reflect the space coupling situation and complexity of aircraft.At the same time,it can precisely describe the actual operation of civil aviation in China.展开更多
In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was ut...In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.展开更多
Complex weather conditions is meaning thunderstorm freezing turbulence wind-shear low visibility weather affect the flight safety.When confronted with complex weather conditions,the controllers should know the weather...Complex weather conditions is meaning thunderstorm freezing turbulence wind-shear low visibility weather affect the flight safety.When confronted with complex weather conditions,the controllers should know the weather condition and trend weather,and notify the aircraft under your control zone.The controllers provide the required services to the pilots,help the pilots to avoid the complex weather.In this paper,through different complex weathers under different control command,get the different methods of control.展开更多
为提高复杂交通场景下车辆目标检测模型的检测精度,以YOLOv8n(you only look once version 8 nano)为基准模型,设计具有复合主干的Neck-ARW(包括辅助检测分支、RepBlock模块、加权跳跃特征连接)颈部结构,减少信息瓶颈造成沿网络深度方...为提高复杂交通场景下车辆目标检测模型的检测精度,以YOLOv8n(you only look once version 8 nano)为基准模型,设计具有复合主干的Neck-ARW(包括辅助检测分支、RepBlock模块、加权跳跃特征连接)颈部结构,减少信息瓶颈造成沿网络深度方向的信息丢失;引入RepBlock结构重参数化模块,在训练过程中采用多分支结构提高模型特征提取性能;添加P2检测层捕捉更多小目标细节特征,丰富网络内小目标的特征信息流;采用Dynamic Head自注意力机制检测头,将尺度感知、空间感知和任务感知自注意力机制融合到统一框架中,提高检测性能;采用基于层自适应幅度的剪枝(layer-adaptive magnitude based pruning,LAMP)算法,移除模型的冗余参数,构建YOLO-NPDL(Neck-ARW,P2,Dynamic Head,LAMP)车辆目标检测模型。以UA-DETRAC(university at Albany detection and tracking)数据集为试验数据集,分别进行RepBlock模块嵌入位置试验、不同颈部结构对比试验、剪枝试验、消融试验、模型性能对比试验,验证YOLO-NPDL模型的平均精度均值。试验结果表明:RepBlock模块同时嵌入辅助检测分支和颈部主干结构时对多尺度目标的特征提取能力更优,在训练过程中可保留更多的细节信息,但参数量和计算量均增大;采用Neck-ARW颈部结构后模型的平均精度均值E mAP50、E mAP50-95分别提高1.1%、1.7%,参数量减小约17.9%,结构较优;剪枝率为1.3时,模型参数量、计算量分别减小约38.0%、24.0%,冗余通道占比较少,结构较紧凑;与YOLOv8n模型相比,YOLO-NPDL模型在参数量基本相同的基础上,召回率增大2.7%,E mAP50增大2.7%,达到94.7%,E mAP50-95增大6.4%,达到79.7%;与目前广泛使用的YOLO系列模型相比,YOLO-NPDL模型在较少参数量的基础上,检测精度较高。YOLO-NPDL模型在检测远端目标、雨天及夜景等实际复杂交通情景中无明显误检、漏检情况,可检测到更多的远端小目标车辆,检测效果更优。展开更多
文摘The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of survivability.In this paper,we propose a model for survivability quantification,which is acceptable for networks carrying complex traffic flows.Complex network traffic is considered as general multi-rate,heterogeneous traffic,where the individual bandwidth demands may aggregate in complex,nonlinear ways.Blocking probability is the chosen measure for survivability analysis.We study an arbitrary topology and some other known topologies for the network.Independent and dependent failure scenarios as well as deterministic and random traffic models are investigated.Finally,we provide survivability evaluation results for different network configurations.The results show that by using about 50%of the link capacity in networks with a relatively high number of links,the blocking probability remains near zero in the case of a limited number of failures.
基金the financial support of the National Science Foundation of China Project:U1964203 and 52072215National key R&D Program of China:2020YFB1600303.
文摘The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To efficiently search for critical scenarios with the trigger of complex traffic flow,this scenario is encoded as genes and it is regenerated through selection,mutation,and crossover iteration processes,known as the Genetic Algorithm(GA).Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios,contributing to the construction of the SOTIF scenario library.
基金co-supported by the State Key Program of National Natural Science Foundation of China (No. 91538204)the National Science Fund for Distinguished Young Scholars (No. 61425014)the National Key Technologies R&D Program of China (No. 2015BAG15B01)
文摘Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.
基金supported by the National Natural Science Foundation of China (Nos.U1833103, 71801215, U1933103)the Fundamental Research Funds for the Central Universities (No.3122019129)。
文摘Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.
基金This work was supported by the Na⁃tional Natural Science Foundation of China(No.61903187)Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20190732)。
文摘Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.
基金supported by the National Natural Science Foundation of China (No. 61573181)the Civil Aviation Joint Fund Key Projects of National Natural Science Foundation of China (No.U1333202)
文摘It is an important issue to assess traffic situation complexity for air traffic management.There is a lack of systematic review of the existing air traffic complexity assessment methods,and there is no consideration of the role of airspace and traffic coordination mechanism.A new 3-D airspace complexity measurement method is proposed based on route structure constraints to evaluate the air traffic complexity objectively.Firstly,the model of the impact on horizontal and vertical direction for“aircraft pair”is established based on the route guidance.After that,the coupled complexity model for 3-D airspace is given according to the modification on the model in terms of flight standardization.Finally,the global model of the airspace traffic complexity is established.It is proved by the experimental data from the actual operation in airspace that the proposed model can reflect the space coupling situation and complexity of aircraft.At the same time,it can precisely describe the actual operation of civil aviation in China.
基金co-supported by the National Natural Science Foundation of China(No.61304190)the Fundamental Research Funds for the Central Universities of China(No.NJ20150030)the Youth Science and Technology Innovation Fund(No.NS2014067)
文摘In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.
文摘Complex weather conditions is meaning thunderstorm freezing turbulence wind-shear low visibility weather affect the flight safety.When confronted with complex weather conditions,the controllers should know the weather condition and trend weather,and notify the aircraft under your control zone.The controllers provide the required services to the pilots,help the pilots to avoid the complex weather.In this paper,through different complex weathers under different control command,get the different methods of control.
文摘为提高复杂交通场景下车辆目标检测模型的检测精度,以YOLOv8n(you only look once version 8 nano)为基准模型,设计具有复合主干的Neck-ARW(包括辅助检测分支、RepBlock模块、加权跳跃特征连接)颈部结构,减少信息瓶颈造成沿网络深度方向的信息丢失;引入RepBlock结构重参数化模块,在训练过程中采用多分支结构提高模型特征提取性能;添加P2检测层捕捉更多小目标细节特征,丰富网络内小目标的特征信息流;采用Dynamic Head自注意力机制检测头,将尺度感知、空间感知和任务感知自注意力机制融合到统一框架中,提高检测性能;采用基于层自适应幅度的剪枝(layer-adaptive magnitude based pruning,LAMP)算法,移除模型的冗余参数,构建YOLO-NPDL(Neck-ARW,P2,Dynamic Head,LAMP)车辆目标检测模型。以UA-DETRAC(university at Albany detection and tracking)数据集为试验数据集,分别进行RepBlock模块嵌入位置试验、不同颈部结构对比试验、剪枝试验、消融试验、模型性能对比试验,验证YOLO-NPDL模型的平均精度均值。试验结果表明:RepBlock模块同时嵌入辅助检测分支和颈部主干结构时对多尺度目标的特征提取能力更优,在训练过程中可保留更多的细节信息,但参数量和计算量均增大;采用Neck-ARW颈部结构后模型的平均精度均值E mAP50、E mAP50-95分别提高1.1%、1.7%,参数量减小约17.9%,结构较优;剪枝率为1.3时,模型参数量、计算量分别减小约38.0%、24.0%,冗余通道占比较少,结构较紧凑;与YOLOv8n模型相比,YOLO-NPDL模型在参数量基本相同的基础上,召回率增大2.7%,E mAP50增大2.7%,达到94.7%,E mAP50-95增大6.4%,达到79.7%;与目前广泛使用的YOLO系列模型相比,YOLO-NPDL模型在较少参数量的基础上,检测精度较高。YOLO-NPDL模型在检测远端目标、雨天及夜景等实际复杂交通情景中无明显误检、漏检情况,可检测到更多的远端小目标车辆,检测效果更优。
