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This research study explores the use of Bayesian Network Modeling and Video Surveillance to monitor and predict human errors in air traffic control to enhance aviation safety. It aims to quantify the correlation between anomalous human behaviors, errors, and accidents through real-time monitoring algorithms.
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Automated Human Performance Monitoring for Air Traffic Control Safety through Bayesian Network Modeling and Video Surveillance Zhe Sun1, Jiawei Chen1, Rui Li1, Shuai Li2, Pingbo Tang1,*, Yongming Liu3 1School of Sustainable Engineering and the Built Environment, Arizona State University, USA 2School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA 3School for Engineering of Matter, Transport & Energy, Arizona State University, USA
Outline Problem Statement Motivation Potential Solutions Conclusion 36th International System Safety Conference, Aug. 13 – 17, 2018
Problem Statement The national airspace and the airport become more crowded due to the increasing air traffic volume, the number of accidents/incidents increased significantly. Human error is considered as one of the major factors that contribute to more than 70% of all aviation accidents in the United States. 36th International System Safety Conference, Aug. 13 – 17, 2018
Problem Statement 36th International System Safety Conference, Aug. 13 – 17, 2018
Problem Statement Air Traffic Controller (ATC) Errors: Greater than 60% anomalous human behavior (i.e. distraction, fatigue) 36th International System Safety Conference, Aug. 13 – 17, 2018
Motivation How to quantitatively assess the probabilistic dependence between anomalous human behaviors and human errors that eventually lead to near-ground accidents/incidents is important for real-time risk prediction and assessment. Near-ground Accidents/ Incidents Anomalous Behaviors Human Errors 36th International System Safety Conference, Aug. 13 – 17, 2018
Motivation • The overall goal of this study could help answering two questions: • How human error occurs? • What type of human errors are more likely to occur due to anomalous behaviors? • How accident occurs? • What type of accidents/incidents are more likely to occur due to certain human errors? 36th International System Safety Conference, Aug. 13 – 17, 2018
Potential Solution Accident/Incident Report Analysis Video Surveillance 36th International System Safety Conference, Aug. 13 – 17, 2018
Potential Solution Video Surveillance – Distraction Detection Algorithm 36th International System Safety Conference, Aug. 13 – 17, 2018
Potential Solution Video Surveillance – Fatigue Detection Algorithm 36th International System Safety Conference, Aug. 13 – 17, 2018
Potential Solution Bayesian Network (BN) Modeling 36th International System Safety Conference, Aug. 13 – 17, 2018
Conclusion Table 1— Distraction and Fatigue Detection Results 36th International System Safety Conference, Aug. 13 – 17, 2018
Conclusion Table 2— Conditional Probability of Human Errors on Distraction and Fatigue Table 3—Conditional Probability of Most Frequent Accidents/Incidents on Human Errors 36th International System Safety Conference, Aug. 13 – 17, 2018
Conclusion • This research proposed a real-time fatigue and distraction monitoring of ATCs to ensure aviation safety through real-time video surveillance and BN modeling. • The developed BN also help quantify the correlation between anomalous human behaviors (distraction and fatigue), human errors and accidents/incidents. • The real-time human behavior monitoring algorithm uses facial expression detection techniques to capture the fatigue and fatigue of ATCs and use as input to feed into the developed BN to get real-time risk assessment during air traffic control. 36th International System Safety Conference, Aug. 13 – 17, 2018
Acknowledgment The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Kai Goebel, Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged. The authors would also like to express our very great appreciation to Dr. Yezhou Yang for his valuable and constructive suggestions during the planning and development of this research work. His willingness to give his time so generously has been very much appreciated. 36th International System Safety Conference, Aug. 13 – 17, 2018
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Thank you Questions ? Contact Information Zhe Sun: zsun43@asu.edu Jiawei Chen: jchen311@asu.edu Rui Li: ruili11@asu.edu Pingbo Tang: tangpingbo@asu.edu Shuai Li: shuaili4@asu.edu Yongming Liu: Yongming.Liu@asu.edu