With the rapid growth of flight flow,the workload of controllers is increasing daily,and handling flight conflicts is the main workload.Therefore,it is necessary to provide more efficient conflict resolution decision-...With the rapid growth of flight flow,the workload of controllers is increasing daily,and handling flight conflicts is the main workload.Therefore,it is necessary to provide more efficient conflict resolution decision-making support for controllers.Due to the limitations of existing methods,they have not been widely used.In this paper,a Deep Reinforcement Learning(DRL)algorithm is proposed to resolve multi-aircraft flight conflict with high solving efficiency.First,the characteristics of multi-aircraft flight conflict problem are analyzed and the problem is modeled based on Markov decision process.Thus,the Independent Deep Q Network(IDQN)algorithm is used to solve the model.Simultaneously,a’downward-compatible’framework that supports dynamic expansion of the number of conflicting aircraft is designed.The model ultimately shows convergence through adequate training.Finally,the test conflict scenarios and indicators were used to verify the validity.In 700 scenarios,85.71%of conflicts were successfully resolved,and 71.51%of aircraft can reach destinations within 150 s around original arrival times.By contrast,conflict resolution algorithm based on DRL has great advantages in solution speed.The method proposed offers the possibility of decision-making support for controllers and reduce workload of controllers in future high-density airspace environment.展开更多
In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algor...In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.展开更多
为了研究管制员飞行冲突调配的人因差错问题,进而有效评估管制员解决飞行冲突的可靠性,以保障空中交通的安全运行,提出系统理论过程分析(System Theoretic Process Analysis, STPA)与认知可靠性与失误分析方法(Cognitive Reliability an...为了研究管制员飞行冲突调配的人因差错问题,进而有效评估管制员解决飞行冲突的可靠性,以保障空中交通的安全运行,提出系统理论过程分析(System Theoretic Process Analysis, STPA)与认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM)相结合的人因可靠性分析方法。首先,通过STPA方法构建系统控制模型,识别不安全控制行为(Unsafe Control Action, UCA)以及致因因素,找到管制员在调配飞行冲突过程中可能存在的差错行为;其次,基于CREAM扩展法对管制员的差错行为进行定量分析,得到管制员调配飞行冲突的人因失误概率。研究显示:使用该方法能够系统、全面地识别出管制员在调配飞行冲突过程中出现的差错行为,进而计算管制员飞行冲突调配的人因失误概率。实例分析表明该方法可以预测管制员在飞行冲突调配过程中的人因失误概率及可靠性,为管制员人因可靠性分析提供了新思路。展开更多
为识别飞行冲突事故的诱发事件与关键演化链路,提出了一种基于事理图谱的事故分析方法。首先依据系统理论过程分析(System Theoretic Process Analysis,STPA)方法辨识飞行冲突场景潜在风险事件,归纳出描述各组件交互过程的场景分析框架...为识别飞行冲突事故的诱发事件与关键演化链路,提出了一种基于事理图谱的事故分析方法。首先依据系统理论过程分析(System Theoretic Process Analysis,STPA)方法辨识飞行冲突场景潜在风险事件,归纳出描述各组件交互过程的场景分析框架;再利用自然语言处理方法从事故文本中提取结构化事件,基于语义相似度匹配建立与风险事件的映射,组成事件演化链条,构建事理图谱;通过对飞行冲突场景进行仿真,运用统计指标定量分析网络拓扑结构,揭示事件对事故演化过程的影响力。结果表明,构建的事理图谱可视化诊断出机组突破管制员指令、机组操作不当导致偏航、航空器机上通讯导航设备故障、管制员纠正指令发布过晚、空中交通预警和防撞系统(Traffic Alert and Collision Avoidance System,TCAS)发出异常措施通告/活动通告(Resolution Advisory/Traffic Advisory,RA/TA)警告、航空器出现飞行汇聚趋势6个诱发事件与3条关键演化链路,其中机组突破管制员指令与管制员指令发布过晚为重点管控事件。展开更多
基金supported by Safety Ability Project of Civil Aviation Administration of China(No.TM 2018-5-1/2)the Open Foundation project of The Graduate Student Innovation Base,China(Laboratory)of Nanjing University of Aeronautics and Astronautics,China(No.kfjj20190720)。
文摘With the rapid growth of flight flow,the workload of controllers is increasing daily,and handling flight conflicts is the main workload.Therefore,it is necessary to provide more efficient conflict resolution decision-making support for controllers.Due to the limitations of existing methods,they have not been widely used.In this paper,a Deep Reinforcement Learning(DRL)algorithm is proposed to resolve multi-aircraft flight conflict with high solving efficiency.First,the characteristics of multi-aircraft flight conflict problem are analyzed and the problem is modeled based on Markov decision process.Thus,the Independent Deep Q Network(IDQN)algorithm is used to solve the model.Simultaneously,a’downward-compatible’framework that supports dynamic expansion of the number of conflicting aircraft is designed.The model ultimately shows convergence through adequate training.Finally,the test conflict scenarios and indicators were used to verify the validity.In 700 scenarios,85.71%of conflicts were successfully resolved,and 71.51%of aircraft can reach destinations within 150 s around original arrival times.By contrast,conflict resolution algorithm based on DRL has great advantages in solution speed.The method proposed offers the possibility of decision-making support for controllers and reduce workload of controllers in future high-density airspace environment.
基金Supported by the Joint Project of National Natural Science Foundation of ChinaCivil Aviation Administration of China(U1333116)
文摘In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.
文摘为了研究管制员飞行冲突调配的人因差错问题,进而有效评估管制员解决飞行冲突的可靠性,以保障空中交通的安全运行,提出系统理论过程分析(System Theoretic Process Analysis, STPA)与认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM)相结合的人因可靠性分析方法。首先,通过STPA方法构建系统控制模型,识别不安全控制行为(Unsafe Control Action, UCA)以及致因因素,找到管制员在调配飞行冲突过程中可能存在的差错行为;其次,基于CREAM扩展法对管制员的差错行为进行定量分析,得到管制员调配飞行冲突的人因失误概率。研究显示:使用该方法能够系统、全面地识别出管制员在调配飞行冲突过程中出现的差错行为,进而计算管制员飞行冲突调配的人因失误概率。实例分析表明该方法可以预测管制员在飞行冲突调配过程中的人因失误概率及可靠性,为管制员人因可靠性分析提供了新思路。
