A Risk Assessment Method of Multi-aircraft Interaction for Complex Airspace
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摘要: 为评估复杂交通场景下的多航空器的交互风险，由空中交通风险与势场理论的相似性，创新性地提出了“多航空器与空域环境的交互势场”概念。构造以航空器、空域点关键及航路为势场源的交互势场，并分别提出了航空器、空域关键点及航路交互势场的生成函数模型；构造随时间变化的历史航迹势场模型，提出考虑历史航迹的短期影响效应的交互势场修正方法；考虑航空器在水平与垂直维度的多场景安全间隔标准，求解满足安全间隔标准的交互势场生成函数参数；考虑多势场源的交互特征，提出了航空器、空域关键点及航路交互势场的融合机制。借鉴势场力与势能的转化关系，提出了基于势能的航空器交互风险指标RPE，从能量的角度，揭示了多航空器交互以及航空器与空域环境交互的风险变化过程。为验证评估方法的有效性，以国内某真实扇区为场景开展仿真实验，结果表明：①与其他风险表征指标相比，所提的交互风险指标RPE更接近于空中交通管制员感知到的风险指数；②在某些区间，RPE表现的更为灵敏，平均绝对误差为0.077，明显低于传统基于冲突时间的风险指数RATSR。综上，本文所提出的交互风险评估方法有望为未来空中复杂交通场景的风险管理提供更加精确的决策支撑。Abstract: To assess the interaction risks among multiple aircraft in complex traffic scenarios, a concept of "interac-tion potential fields of multiple aircraft and airspace environment" is developed, which is based on the similarity be-tween traffic risk and potential field theory. The interaction potential fields (IPF) generated by aircraft, critical air-space points (CAPs) and air routes (ARs) are defined, respectively, and the generation functions of IPFs are formu-lated. Considering the short-term effects of historical trajectories on the aircraft, a time-varying historical trajectory IPF is added to the real-time aircraft IPFs; considering the requirement of safety intervals in horizontal and vertical dimensions for aircraft, the parameters of rule-compliant IPFs are found; then, a fusion method is developed to integrate IPFs generated by aircraft, CAPs and ARs. Inspired by the relationship between potential field force and poten-tial energy, a potential energy-based risk index is introduced, denoted as RPE, showing the changes of risk over time in multi-aircraft scenarios from the perspective of energy. To validate the effectiveness of the proposed method, a simulation based on a real airspace section is introduced, and the results show that: ① RPE is much closer to the precepted risk by the air traffic operators (RSE) compared with traditional risk indicators; ② RPE is more sensitive at certain intervals than the conflict time-based index RATSR, with a mean absolute error of 0.077. In brief, the pro-posed risk assessment method could offer more precise decision support for risk management in complex air traffic scenarios in the future.
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Key words:
- traffic safety /
- multi-aircraft interaction /
- potential field theory /
- risk assessment
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图 3 势场强度在速度方向上相对于距离的变化
Figure 3. Variation in potential field strength relative to distance in the velocity direction
图 7 势场强度随离空域关键点距离的变化
Figure 7. Variation in potential field strength relative to distance from the waypoint
图 8 航路生成的势场
Figure 8. Variation in potential field strength relative to distance from the route
图 9 势场强度随航路距离的变化
Figure 9. Variation in potential field strength relative to distance from the route
图 10 多航空器、空域关键点、航路势场交互
Figure 10. Variation in potential field strength relative to distance from the route
图 13 管制扇区结构和面向扇区的雷达监视仿真
Figure 13. Control sector structure and Sector-oriented radar surveillance simulation
图 15 航空器RSE风险和RPE风险的混淆矩阵
Figure 15. Confusion matrix of aircraft RSE risk and RPE risk
图 16 航空器RSE风险和RATSR风险的混淆矩阵
Figure 16. Confusion matrix of aircraft RSE risk and RATSR risk
图 17 风险系数随扇区流量、与空域关键点距离的变化趋势
Figure 17. Trend of RPE changes with sector volume and distance from waypoint
表 1 扇区交通流仿真
Table 1. Sector traffic flow simulation
航路段 参数 长度/km 方向角/(°) 交通量/（架/h） 流入量/（架/h） 流出量/（架/h） 重型飞机比例 B213 186 187 5.8 3.1 2.7 0.19 A581 245 218 3.3 1.5 1.8 0.11 I248 162 250 3.1 1.6 1.5 0.14 I793 108 294 4.6 2.1 2.5 0.16 A461 56 62 2.6 1.1 1.5 0.08 R343 57 76 3.2 1.4 1.8 0.11 
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表 2 航空器风险评估结果对比
Table 2. Comparison of aircraft risk assessment results
航空器编号 RPE RATSR RSEm Error_PE Error_ATSR F1001 15.43 0.52 1.50 0.09 0.22 F1002 25.58 0.86 3.50 －0.06 0.16 F1003 18.72 0.61 2.50 －0.03 0.11 F1017 33.62 0.83 4.50 －0.06 －0.07 F1018 31.75 0.82 3.50 0.09 0.12 F1019 25.21 0.63 2.50 0.13 0.13 F1020 7.34 0.25 0.50 0.08 0.15 F1055 9.43 0.21 1.50 －0.06 －0.09 F1056 22.56 0.73 2.50 0.06 0.23 F1057 24.28 0.82 3.50 －0.09 0.12 F1058 31.96 0.83 4.50 －0.10 －0.07 F1059 3.38 0.24 0.50 －0.02 0.14 F1060 24.86 0.76 3.50 －0.08 0.06 F1109 15.28 0.71 2.50 －0.12 0.21 F1110 14.53 0.44 1.50 0.06 0.14 F1111 24.63 0.82 3.50 －0.08 0.12 F1112 38.97 0.84 4.50 0.07 －0.06 F1113 10.69 0.45 1.50 －0.03 0.15 F1114 33.22 0.83 3.50 0.13 0.13 均值 21.65 0.64 2.71 0.077 0.131
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[1] 中国民用航空局. 2017年全国民航航班运行效率报告[R]. 北京: 中国民用航空局运行监控中心, 2018.Civil Aviation Administration of China(CAAC). Report on national civil aviation flight operation efficiency in 2017[R]. Beijing: Center for CAAC Operation Monitoring, 2018. (in Chinese) [2] VISINTINI A, GLOVER W, LYGEROS J, et al, Monte Carlo optimization for conflict resolution in air traffic control[J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(4)470-482. doi: 10.1109/TITS.2006.883108 [3] WANG J, WU J, LI Y. The driving safety field based on driv-er-vehicle-road interactions[J]. IEEE Transactions on Intelli-gent Transportation Systems, 2015, 16(4): 2203-2214. doi: 10.1109/TITS.2015.2401837 [4] 朱永文, 谢华, 蒲钒, 等. 空域网格化方法及其在空管中的应用研究[J]. 航空工程进展, 2021, 12(4): 12-24.ZHU Y W, XIE H, PU F, et al. Spatial grid method and its application in air traffic control[J]. Advances in Aeronauti-cal Science and Engineering, 2021, 12(4): 12-24. (in Chinese) [5] 王红勇, 温瑞英. 基于复杂网络的空中交通态势风险评估方法[J]. 中国安全科学学报, 2018, 28(5): 172-178.WANG H Y, WEN R Y. Complex network-based risk assess-ment method for air traffic posture[J] China Safety Science Journal, 2018, 28(5): 172-178. (in Chinese) [6] LI B, DU W, ZHANG Y, et al. A deep unsupervised learning approach for airspace complexity evaluation[J]. IEEE Trans-actions on Intelligent Transportation Systems, 2022, 23(8): 11739-11751. doi: 10.1109/TITS.2021.3106779 [7] JIANG Y, WANG S, YAO Z, et al. A cellular automata model for mixed traffic flow considering the driving behavior of con-nected automated vehicle platoons[J]. Physica A, 2021, 582: 126262. doi: 10.1016/j.physa.2021.126262 [8] LI L, WANG C, ZHANG Y. Microscopic state evolution mod-el of mixed traffic flow based on potential field theory[J]. Physica A, 2022, 607: 128185. doi: 10.1016/j.physa.2022.128185 [9] BYRNE S, NAEEM W, FERGUSON S. Improved APF strate-gies for dual-arm local motion planning[J]. Transaction of the Institute of Measurement and Control, 2015, 37(1): 73-90. doi: 10.1177/0142331214532002 [10] WOLF M T, BURDICK J W. Artificial potential functions for highway driving with collision avoidance. [C]. IEEE In-ternational Conference on Robotics Automation, Pasadena: IEEE, 2008. [11] 陈永, 贺红, 张薇, 等. 基于场力的驾驶员影响因素交通流动力学模型[J]. 力学学报, 2018, 50(5): 1219-1234.CHEN Y, HE H, ZHANG W, et al. A driver influenced fac-tor traffic flow dynamics model based on field forces[J]. Chi-nese Journal of Theoretical and Applied Mechanics, 2018, 50(5): 1219-1234. (in Chinese) [12] NI D, WANG H. A unified perspective on traffic flow theory Part Ⅲ: validation and benchmarking. [J]. Applied Mathe-matical Sciences, 2013, 7(40): 1965-1982. [13] WANG J, WU J, ZHENG X, et al. Driving safety field theo-ry modeling and its application in precollision warning sys-tem[J]. Transportation Research Part C: Emerging Technolo-gies, 2016, 72: 306-324. doi: 10.1016/j.trc.2016.10.003 [14] 管祥民, 吕人力. 基于混合人工势场与蚁群算法的多飞行器冲突解脱方法[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(1): 28-33. doi: 10.3963/j.issn.2095-3844.2020.01.006GUAN X M, LYU R L. Conflict resolution method for multi-ple aircraft based on hybrid artificial potential field and ant colony algorithm[J]. Journal of Wuhan University of Tech-nology(Transportation Science & Engineering), 2020, 44(1): 28-33(in Chinese). doi: 10.3963/j.issn.2095-3844.2020.01.006 [15] LIU W, LI Z. Comprehensive predictive control method for automated vehicles in dynamic traffic circumstances[J]. IET Intelligent Transport Systems, 2018, 12(10): 1455-1463. doi: 10.1049/iet-its.2018.5142 [16] LI M, SONG X, CAO H, et al. Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field[J]. Mechanical Systems and Signal Processing, 2019, 124: 199-216. doi: 10.1016/j.ymssp.2019.01.040 [17] 庞笔照. 航路网络关键结构分析及优化[D]. 天津: 中国民航大学, 2019.PANG B Z. Critical structure analysis and optimization for air route network[D]. Tianjing: Civil Aviation University of China, 2019. (in Chinese) [18] 张旭. 繁忙空域的临时航线划设与评估研究[D]. 天津: 中国民航大学, 2021.ZHANG X. Study on temporary route planning and evalua-tion in busy airspace[D]. Tianjing: Civil Aviation University of China, 2021. (in Chines) [19] LI L H, GAN J, QU X, et al. A novel lane-changing model of connected and automated vehicles: using the safety potential field theory[J]. Physica A, 2020, 559: 125039. doi: 10.1016/j.physa.2020.125039 [20] LI L H, GAN J, QU X, et al. Dynamic driving risk potential field model under the connected and automated vehicles en-vironment and its application in car-following modeling[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 122-141. doi: 10.1109/TITS.2020.3008284 [21] 徐涛, 陈雪蕊, 吕宗平. 基于航迹聚类的终端区飞行程序轨迹表示[J]. 四川大学学报(工程科学版), 2016, 48(6): 188-196.XU T, CHEN X R, LYU Z P. Flight procedure representation of terminal area based on flight tracks clustering[J]. Advanced Engineering Sciences, 2016, 48(6): 188-196. (in Chinese) [22] AI Y, LI Y Y, HAN X, et al. Real-time risk assessment meth-od for multi-aircraft interaction based on potential field theo-ry[J]. Physica A: Statistical Mechanics and its Applications, 2024, 633: 129423. doi: 10.1016/j.physa.2023.129423 [23] 冯霞, 孙琦琦, 左海超. 基于Informer的客机长时4D航迹预测方法[J]. 交通信息与安全, 2023, 41(4): 111-121.FENG X, SUN Q Q, ZUO H C. A method for predicting long-term 4D trajectory of airplanes based on informer[J]. Journal of Transport Information and Safety, 2023, 41(04): 111-121. (in Chinese) [24] 纪新雨, 初建宇, 李印凤, 等. 基于改进CURE算法的终端区航迹聚类[J]. 指挥信息系统与技术, 2021, 12(6): 63-67.JI X Y, CHU J Y, LI Y F, et al. Terminal area trajectory cluster-ing based on improved cure algorithm[J]. Command Informa-tion System and Technology, 2021, 12(6): 63-67. (in Chinese) -
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