A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents.

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.

Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots.

The study aimed at investigating the impact of an innovative Wake Vortex Alert (WVA) avionics on pilots' operation and mental states, intending to improve aviation safety by mitigating the risks associated with wake vortex encounters (WVEs). Wake vortices, generated by jet aircraft, pose a significant hazard to trailing or crossing aircrafts. Despite existing separation rules, incidents involving WVEs continue to occur, especially affecting smaller aircrafts like business jets, resulting in aircraft upsets and occasional cabin injuries. To address these challenges, the study focused on developing and validating an alert system that can be presented to air traffic controllers, enabling them to warn flight crews. This empowers the flight crews to either avoid the wake vortex or secure the cabin to prevent injuries. The research employed a multidimensional approach including an analysis of human performance and human factors (HF) issues to determine the potential impact of the alert on pilots' roles, tasks, and mental states. It also utilizes Human Assurance Levels (HALs) to evaluate the necessary human factors support based on the safety criticality of the new system. Realistic flight simulations were conducted to collect data of pilots' behavioural, subjective and neurophysiological responses during WVEs. The data allowed for an objective evaluation of the WVA impact on pilots' operation, behaviour and mental states (mental workload, stress levels and arousal). In particular, the results highlighted the effectiveness of the alert system in facilitating pilots' preparation, awareness and crew resource management (CRM). The results also highlighted the importance of avionics able to enhance aviation safety and reducing risks associated with wake vortex encounters. In particular, we demonstrated how providing timely information and improving situational awareness, the WVA will minimize the occurrence of WVEs and contribute to safer aviation operations.

Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots' and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.

Pilot Perceptions of Wire Strikes in Agricultural Aviation Operations.

INTRODUCTION: Agricultural aircraft operations are associated with unique challenges. In particular, these include maintaining awareness of obstacles associated with flight at very low altitudes. Wire strikes are a common cause of accidents in these operations.METHODS: Focus groups were completed during the 2022 Ag Aviation Expo hosted by the National Agricultural Aviation Association with pilots who had experienced wire-strike events (N = 22). The researchers coded the transcripts using a human factors framework.RESULTS: Notably, unplanned "trim passes" were a key stage of flight during wire-strike events. Cognitive risk factors that may have affected their performance included situation awareness, decision-making choices, and pressure to perform. Over half of subjects reported being aware of the wire before collision. Possible prevention strategies include not spraying the field due to safety risks, paying better attention to where they were in the field, and avoiding deviation from the planned route.DISCUSSION: Wire-strike events often occur due to momentary lapses in attention, even when the pilot is already aware of the wire. This study shows that targeted approaches to prevent wire strikes in agricultural aviation operations require addressing a number of cognitive risks and human factors, rather than implementing increased preflight surveillance. These results have implications for preventing future wire-strike accidents based directly on pilot perceptions, both within agricultural operations and general aviation more broadly.Baumgartner HM, DiDomenica R, Hu PT, Thomas S. Pilot perceptions of wire strikes in agricultural aviation operations. Aerosp Med Hum Perform. 2024; 95(6):305-312.

Flight safety assessment based on a modified human error risk quantification approach.

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents.

Effects of distance flown on pilot decision making in continued flight into deteriorating weather conditions.

INTRODUCTION: Continuing flight into adverse weather remains a significant problem in general aviation (GA) safety. A variety of experiential, cognitive, and motivational factors have been suggested as explanations. Previous research has shown that adverse weather accidents occur further into planned flights than other types of accident, suggesting that previous investment of time and effort might be a contributing factor. The aim of this study was to experimentally determine the effect of prior commitment on general aviation pilots' decision-making and risk-taking in simulated VFR flights. METHOD: Thirty-six licensed pilots 'flew' two simulated flights designed to simulate an encounter with deteriorating coastal weather and a developing extensive cloud base underneath the aircraft as it crossed a mountain range. After making a decision to continue or discontinue the flight, pilots completed a range of risk perception, risk taking, and situational awareness measures. RESULTS: Visual flight rules were violated in 42% of the flights. Prior commitment, in terms of distance already flown, led to an increased tendency to continue the flight into adverse weather in the coastal 'scud running' scenario. Continuing pilots perceived the risks differently and showed greater risk tolerance than others. These 'bolder' pilots also tended to be more active and better qualified than the others. CONCLUSIONS: There are undoubtedly multiple factors underlying any individual decision to continue or discontinue a flight. The willingness to tolerate a higher level of risk seems to be one such factor. This willingness can increase with time invested in the flight and also seems to be related to individual flight qualifications and experience. PRACTICAL APPLICATIONS: All pilots might benefit from carefully structured simulator sessions designed to safely teach practical risk management strategies with clear and immediate feedback.

The Untapped Potential of Narrative as a Tool in Aviation Mental Health and Certification.

INTRODUCTION: Work-related stress is common in pilots, with broad implications, including the potential development of mental health symptoms and sometimes even psychiatric disease. This commentary argues for the use of narrative as a tool to promote preventive health behaviors in pilots and combat misinformation about aeromedical certification related to mental health.Hoffman WR, McNeil M, Tvaryanas A. The untapped potential of narrative as a tool in aviation mental health and certification. Aerosp Med Hum Perform. 2024; 95(3):165-166.

Analysis on coupling dynamic effect of human errors in aviation safety.

Human factors have increasingly been the leading cause of aircraft accidents. In most cases, human factors are not working alone, instead they are coupled with complex environment, mechanical factors, physiological and psychological factors of pilots, and organizational management, all of which form a complex aviation safety system. It is vital to investigate the coupling impact of human errors to avoid the occurrence of aviation accidents. In view that the Human Factors Analysis and Classification System (HFACS) provides a hierarchical classification principle of human errors in aviation accidents, and the System Dynamics (SD) approach is helpful to describe the risk evolution process, this paper establishes a hybrid HFACS-SD model by employing the HFACS and the SD approach to reveal the aviation human factors risk evolution mechanism, in which the HFACS is first used to capture the causal factors of human errors risk, and a coupling SD model is then built to describe the evolution of aviation human factors risk supported by historical data. The eigenvalue elasticity analysis is taken to identify critical loops and parameters that have a substantial impact on the system structural behavior, and the influence of parameters and loops is assessed. Simulation results show that the evolution trend of the accident rate can be replicated by the proposed HFACS-SD model, and the structural dominance analysis can efficiently identify critical loops and parameters. Simulation results further show that, with the recommended safety enhancement measures, the stability of the aviation system is increased, and thus lowering the overall accident rate.

Pilot performance and workload whilst using an angle of attack system.

Loss of control in flight is the primary category of fatal accidents within all sectors of aviation and failure to maintain adequate airspeed - leading to a stall - is often cited as a causal factor. Stalls occur when the critical angle of the aircraft is exceeded for a given airspeed. Using airspeed as an indicator of the potential to stall is an unreliable proxy. Systems that measure the angle of attack have been routinely used by military aircraft for over 50 years however rigorous academic research with respect to their effectiveness has been limited. Using a fixed-base flight simulator fitted with a simulated, commercially available angle of attack system, 20 pilots performed normal and emergency procedures during the circuit/pattern in a light aircraft. Experimental results have shown that pilot performance was improved when angle of attack was displayed in the cockpit for normal and emergency procedures during the approach phase of flight in the pattern/circuit. In relation to pilot workload, results indicated that during the approach phase of flight, there was a moderate but tolerable increase in pilot workload. The use of such a display may assist pilots to maintain the aircraft within the optimum range and hence reduce occurrences of unstable approaches. Overall, fewer stall events were observed when angle of attack was displayed and appropriate pilot decisions made during emergencies. These results provide a new perspective on pilot workload and aviation safety.

Prediction of severity of aviation landing accidents using support vector machine models.

The purpose of this study was to apply support vector machine (SVM) models to predict the severity of aircraft damage and the severity of personal injury during an aircraft approach and landing accident and to evaluate and rank the importance of 14 accident factors across 39 sub-categorical factors. Three new factors were introduced using the theory of inattentional blindness: The presence of visual area surface penetrations for a runway, the Federal Aviation Administration's (FAA) visual area surface penetration policy timeframe, and the type of runway approach lighting. The study comprised 1,297 aircraft approach and landing accidents at airports within the United States with at least one instrument approach procedure. Support vector machine models were developed in using the linear, polynomial, radial basis function (RBF), and sigmoid kernels for the severity of aircraft damage and additional SVM models were developed for the severity of personal injury. The SVM models using the RBF kernel produced the best machine learning models with a 96% accuracy for predicting the severity of aircraft damage (0.94 precision, 0.95 recall, and 0.95 F1-score) and a 98% accuracy for predicting the severity of personal injury (0.99 precision, 0.98 recall, and 0.99 F1-score). The top predictors across both models were the pilot's total flight hours, time of the accident, pilot's age, crosswind component, landing runway number, single-engine land certificate, and any obstacle penetration. This study demonstrates the benefit of SVM modeling using the RBF kernel for accident prediction and for datasets with categorical factors.

Cause analysis of unsafe acts of pilots in general aviation accidents in China with a focus on management and organizational factors.

Objectives. General aviation (GA) safety has become a key issue worldwide and pilot errors have grown to be the primary cause of GA accidents. However, fewer empirical studies have examined the contribution of management and organizational factors for these unsafe acts. Flawed decisions at the organizational level have played key roles in the performance of pilots. This study provides an in-depth understanding of the management and organizational factors involved in GA accident reports. Methods. A total of 109 GA accidents in China between 1996 and 2021 were analysed. Among these reports, pilot-related accidents were analysed using the human factors analysis and classification system (HFACS) framework. Results. The significant effects of managerial and organizational factors and the failure pathways on GA accidents have been identified. Furthermore, unlike traditional HFACS-based analyses, the statistically significant relationships between failures at the organizational level and the sub-standard acts of the pilots in GA accidents were revealed. Conclusions. Such findings support that the GA accident prevention strategy that attempts to reduce the number of unsafe acts of pilots should be directed to the crucial causal categories at HFACS organizational levels: resource management, organizational process, failure to correct a known problem, inadequate supervision and supervisory violations.

Cognitive Style and Flight Experience Influence on Confirmation Bias in Lost Procedures.

BACKGROUND: Accident analysis and empirical research have shown that the decision-making process of pilots after becoming lost is adversely affected by confirmation bias; this constitutes a serious threat to aviation safety. However, the underlying mechanism of confirmation bias in the context of lost procedures are still unclear.METHODS: This study used scenario-based map-reading tasks to conduct two experiments to explore the mechanism of confirmation bias in the lost procedures. In Experiment 1, 34 undergraduate students and 28 flying cadets were enrolled in a formal experiment to examine the effects of verbal-imagery cognitive style, experience level, and their interaction on confirmation bias. In Experiment 2, we further explored the influence of strategy as a core component of experience on confirmation bias with 26 flying cadets.RESULTS: The study found that individuals were subject to confirmation bias in lost procedures. Visualizers (M = 0.78, SD = 0.75) were almost twice as likely to select the disconfirmatory features than verbalizers (M = 0.37, SD = 0.49). Visualizers exhibited a lower degree of confirmation bias than verbalizers, and experience helps verbalizers to reduce their degree of confirmation bias. The protective effect of experience mainly lies in individuals' choice of strategy.DISCUSSION: Future aviation safety campaigns could be aimed at adopting a candidate selection process that focuses more on psychological attributes by testing for cognitive style, and enriching individual experience through adequate training. Such measures would reduce confirmation bias.Xu Q, Wang M, Wang H, Liu B, You X, Ji M. Cognitive style and flight experience influence on confirmation bias in lost procedures. Aerosp Med Hum Perform. 2022; 93(8):618-626.

A 5-Year Analysis of Aviation Medical Examinations in South Korea.

INTRODUCTION: Little has been studied to promote aviation health in South Korea. The aim of this study was to analyze the results of aviation medical examinations conducted in South Korea over the past 5 yr and, in doing so, provide evidence for establishing a health promotion, disease prevention plan.METHODS: Subjects of the study consisted of applicants who underwent aviation medical examinations from January 1, 2016, to December 31, 2020.RESULTS: Over the past 5 yr, the total number of aviation medical examinations in South Korea has shown an annual increase of more than 10%. In the presence of the COVID-19 pandemic in 2020, the number of aviation medical examinations, both renewal and initial, for all types of licenses, except renewal aviation medical examinations for the ATCL, decreased. Disqualification rates were generally higher in initial examinations than in renewal examinations. The examination results for license renewal showed the ATPL disqualification rate to be the highest at 3.5 per 1000 pilots per year in their 50s, with cardiology cases being the most common reason for disqualification, followed by ophthalmology cases and psychiatry cases. Diagnostic categories for those disqualified after initial aviation medical examinations were similar, though ophthalmological causes were most common, followed by cases of psychiatry and neurology.CONCLUSION: Main causes of disqualification in airmen and air traffic controllers were identified as the presence of cardiovascular, ophthalmic, and psychiatric diseases. The results of this study provide evidence for improving health promotion activity plans to manage risk factors of such diseases for aviation workers.Kim JS, Han BS, Kwon YH, Lim J. A 5-yr analysis of aviation medical examinations in South Korea. Aerosp Med Hum Perform. 2022; 93(6):499-506.

A Systematic Review of Multilevel Influenced Risk-Taking in Helicopter and Small Airplane Normal Operations.

The violation of aviation rules, particularly meteorological flight rules, can have fatal outcomes. Violation can sometimes be explained by intentional risk-taking, alternatively it can be the manifestation of a strategy to enhance performance and influence outcomes, such as saving time or fulfilling customer expectations. The aim of this study was to determine the types of risk-taking behavior within extant empirical research and identify multilevel antecedents related to risk-taking in the context of aviation operations, via a systematic literature review. 4,742 records were identified, which after screening resulted in the detailed consideration of 10 studies, three qualitative and seven quantitative studies, which met the eligibility criteria. Only published works were included in the review, thus the results may have been subject to publication bias, however, the types of risk taking within the research were consistent with that observed in Australian and New Zealand accident reports. The predominate risk-taking behavior was that of continuing Visual Flight Rules (VFR) flight into deteriorating conditions / Instrument Meteorological Conditions (IMC). Multilevel influences could be categorized under two overarching themes, being "continuation influence" and "acceptance of risk / normalization of deviance." One or both themes was consistently observed across the finding in all studies, although precaution should be given to the relative frequency of the reported associations. This review indicates the value of considering the social and organizational influences on risk-taking, and suggests avenues for future research, in particular exploring the influences through a Self-Determination Theory (SDT) lens.

The development of a scenario-based human-machine-environment-procedure (HMEP) classification scheme for the root cause analysis of helicopter accidents.

The current study analyzed the root causes of 22 helicopter accidents/incidents that took place between 1998 and 2019. Each root cause was coded using three commonly used classification models in aviation HFACS, ATSB, and IATA to identify recurring factors for better targeting of future prevention strategies. The frequency analysis revealed that not following procedure (22 observations), training inadequate or unavailable (17), inadequate regulatory oversight (17), inadequate procedure guidance (16), company management absent or deficient (10) and incorrect manuals/charts/checklists (9) were the most frequent contributing factors. Since none of the existing models could summarize the root causes of 22 occurrences effectively, a scenario-based human-machine-environment-procedure (HMEP) classification scheme was proposed to use organizational influences, people management, technical failure, procedure and document, and environment as the first-layer subcategories. The HMEP scheme was additionally applied to the analysis and coding of 4 helicopter accidents in the USA published by the NTSB. The HMEP scheme revealed that NTSB had identified a significantly greater number of root causes in the manufacturer design, manufacturing & documentation. Overall, HMEP can be used to guide the data collection during accident investigation and subsequently to aggregate aviation accidents to derive recurring factors and compare accident patterns in an efficient manner.

Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario.

Engine damage as a consequence of foreign object debris (FOD) during flight is frequently caused by birds. One approach to minimising disruption caused by this damage is to provide flight crew with accurate information relating to the continuing operational status of the aircraft's engines. Before designing such avionic systems however, understanding of current procedures is needed. Hierarchical Task Analysis (HTA) and Systematic Human Error Reduction and Prediction Approach (SHERPA) were used to identify potential failures that flight crew may make when managing an engine bird strike. Workshops with commercial pilots generated insights into current practice and a commercial pilot SME reviewed outputs for accuracy. Over 200 potential failures were identified, most commonly related to communication. Remedial measures, considering future avionic systems, are proposed to mitigate identified failures. This analysis provides a starting point for future design concepts for assisting flight crew in dealing with engine malfunction due to FOD strikes. Practitioner summary: Hierarchical Task Analysis was conducted to show all tasks involved in dealing with an in-flight aircraft engine bird strike. Systematic Human Error Reduction and Prediction Approach analysis was performed and over 200 possible failures were identified when managing this event. Remedial measures are proposed to help mitigate possible failures.

An Orientation to Aviation Psychiatry: With 30 Case Examples and a 10-Year Follow-up.

ABSTRACT: This report orients general psychiatrists to assessment and care for "safety sensitive" aviation workers. Our case study of 30 sequential aviation patients consists of demographic, clinical, and aviation characteristics plus a 10-year follow-up. Relatively few pilots and other aviation workers self-identified their psychiatric condition. Aviation outcomes associated with psychiatric disorder included personnel injury and/or aircraft damage (three cases), imminent risk without injury or damage (nine cases), impaired aviation functioning without imminent risk (15 cases), and neither risk nor psychiatric disorder or impairment (three cases). Mood, anxiety, and substance use disorders comprised the most common diagnoses. Ten years later, 23 patients were employed (21 in aviation); seven were disabled or deceased. General psychiatrists will find aviation cases clinically familiar but requiring added evaluation for aviation safety and potential interaction with the Federal Aviation Agency and airline supervisors.

Research on Aviation Safety Prediction Based on Variable Selection and LSTM.

Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness.

Cardiovascular Concerns from COVID-19 in Pilots.

BACKGROUND: Cardiovascular disease, now complicated by the COVID-19 pandemic, remains a leading cause of death and risk for sudden incapacitation for pilots during flight. The capacity for aeromedically significant cardiovascular sequelae with potentially imperceptible clinical symptoms elicits concern both during and following resolution of acute COVID-19 in pilots.OBJECTIVE: We summarize the current state of knowledge regarding COVID-19 cardiovascular implications as applied to the aviation environment to better understand their significance toward flight safety and application toward a focused cardiovascular screening protocol following recovery from infection.METHODS: A narrative review of the cardiovascular implications of COVID-19 infection was performed using the PubMed literature search engine and existing organizational guidelines. In addition, to established medical aviation benchmarks, surrogate populations examined included high performance athletes (as a correlate for high G-forces), and scuba divers (as an environmental work analog). Conditions of primary concern included myocardial injury, proarrhythmic substrates, risk of sudden death, myopericarditis, pulse orthostatic lability in response to vigorous activity, cardiovagal dysfunction, and thromboembolic disease.LITERATURE REVIEW: Cardiovascular screening guideline recommendations post-infection recovery are suggested based on profile stratification: airperson flight class, tactical military, and aerobatic pilots. This provides an approach to inform aeromedical decision making.CONCLUSION: Aviation medical examiners should remain cognizant of the clinically apparent and occult manifestations of cardiovascular dysfunction associated with COVID-19 infection when applying return-to-work screening guidelines. This will ensure high flight safety standards are maintained and sudden incapacitation risk mitigated during and following the ongoing pandemic.Elkhatib W, Herrigel D, Harrison M, Flipse T, Speicher L. Cardiovascular concerns from COVID-19 in pilots. Aerosp Med Hum Perform. 2022; 93(12):855-865.