Pilot Behavior Research Articles

Relational Maneuvering of Leader-Follower Unmanned Aerial Vehicles for Flexible Formation.

In this article, we propose a new formation scheme for a leader-follower unmanned aerial vehicle (UAV) system inspired by a human pilot's behavior wherein the formation geometry does not necessarily remain fixed as the vehicles maneuver. In other words, the position and the orientation of the follower with respect to the leader are subject to change as they maneuver while satisfying some constraints. Our strategy ensures that the follower UAV maintains a desired fixed relative distance with respect to the leader UAV, whereas its orientation with respect to the leader UAV may change to reduce its control effort and provide it with a tactical advantage. We call this new relational maneuvering scheme flexible since the set of feasible positions for the follower UAV is not fixed, as is common in close proximity two-ship formations in air-to-air combat. By assigning the follower UAV's linear and angular velocities as its control inputs, our approach tries to emulate a human pilot's behavior in UAVs by taking anticipatory maneuvers when the leader UAV makes aggressive turns. The proposed flexible-geometry formation scheme is robust to the leader's maneuver changes since the follower UAV's control law does not need the information of the leader's angular speed control and only uses relative measurements. This makes the design lucrative even when the vehicles are heterogeneous, global measurements are unavailable, or if the leader UAV is noncooperative. Finally, we present multiple simulations to highlight the merits of the flexible formation control laws.

Cognitive Biases in Commercial Aviation: Empirical Review of Accident Reports

Cognitive biases in commercial aviation can coax pilots into disregarding established protocols or overlooking potential hazards, a concern that the Federal Aviation Administration (FAA) has acknowledged. However, the concrete impact of these biases on aviation operations has not been adequately quantified; empirical evidence remains limited. Recognizing this research gap, our study seeks to provide a comprehensive analysis of cognitive biases within commercial aviation. We conducted a review of National Transportation Safety Board flight incident reports for air carriers across the United States, from 2014 to 2024. We also conducted an expert interview with an instructor who is an experienced pilot to ascertain current training material and its adequacy in addressing cognitive biases. Our analysis revealed cognitive biases not identified by the FAA (e.g., overconfidence) and training material gaps. Our research lays the groundwork for improved training protocols and the potential for a more nuanced understanding of pilot behavior and safety.

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots' psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots' psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots' psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots' psychological workload.

The bench to community initiative: community-based participatory research model for translating research discoveries into community solutions.

The BCI program was established to understand sociocultural determinants of personal care product use, evaluate the biological impact of endocrine disrupting chemicals, and develop community interventions. The three pillars of the program include research, outreach and engagement as well as advocacy activities. The research pillar of the BCI includes development of multidisciplinary partnerships to understand the sociocultural and biological determinants of harmful chemical (e.g., endocrine disrupting chemicals) exposures from personal care products and to implement community interventions. The outreach and engagement pillar includes education and translation of research into behavioral practice. The research conducted through the initiative provides the foundation for advocacy engagement with applicable community-based organizations. Essential to the mission of the BCI is the participation of community members and trainees from underrepresented backgrounds who are affected by breast cancer disparities. Two behavioral interventions will be developed building on prior research on environmental exposures with the focus on personal care products including findings from the BCI. In person and virtual education activities include tabling at community events with do-it-yourself product demonstrations, Salon Conversations-a virtual platform used to bring awareness, education, and pilot behavior change interventions, biennial symposiums, and social media engagement. BCI's community advisory board members support activities across the three pillars, while trainees participate in personal and professional activities that enhance their skills in research translation. This paper highlights the three pillars of the BCI, lessons learned, testimonies from community advisory board members and trainees on the impact of the initiative, as well as BCI's mission driven approaches to achieving health equity.

VALIO: Visual attention-based linear temporal logic method for explainable out-of-the-loop identification

The phenomenon of being Out-Of-The-Loop (OOTL) can significantly undermine pilots’ performance and pose a threat to aviation safety. Previous attempts to identify OOTL status have primarily utilized “black-box” machine learning techniques, which fail to provide explainable insights into their findings. To address this gap, our study introduces a novel application of Linear Temporal Logic (LTL) methods within a framework named Visual Attention LTLf for Identifying OOTL (VALIO), leveraging eye-tracking technology to non-intrusively capture the pilots’ attentional focus. By encoding Areas of Interest (AOIs) and gaze durations within the cockpit into Visual Attention Traces (VAT), the method captures the spatial and temporal dimensions of visual attention. It enables the LTL methods to generate interpretable formulas that classify pilot behaviors and provide insights into the understanding of the OOTL phenomenon. Through a case study of a simulated flight experiment, we compared the efficacy of this approach using different time windows from 10 s to 75 s. The results demonstrate that VALIO’s performance is stable across all time windows with the best F1 score of 0.815 and the lowest F1 of 0.769. And it significantly outperforms the other machine learning methods when using time windows shorter than 30 s, signifying its ability to detect the OOTL status more in-timely. Moreover, the VALIO elucidates pilot behaviors through the derivation of human-readable LTLf formulas, offering the explainability of the results and insights into OOTL characteristics. Overall, this research proposes the VALIO framework as an improvement for OOTL identification in both performance and explainability.

Coordination of gaze and action during high-speed steering and obstacle avoidance.

When humans navigate through complex environments, they coordinate gaze and steering to sample the visual information needed to guide movement. Gaze and steering behavior have been extensively studied in the context of automobile driving along a winding road, leading to accounts of movement along well-defined paths over flat, obstacle-free surfaces. However, humans are also capable of visually guiding self-motion in environments that are cluttered with obstacles and lack an explicit path. An extreme example of such behavior occurs during first-person view drone racing, in which pilots maneuver at high speeds through a dense forest. In this study, we explored the gaze and steering behavior of skilled drone pilots. Subjects guided a simulated quadcopter along a racecourse embedded within a custom-designed forest-like virtual environment. The environment was viewed through a head-mounted display equipped with an eye tracker to record gaze behavior. In two experiments, subjects performed the task in multiple conditions that varied in terms of the presence of obstacles (trees), waypoints (hoops to fly through), and a path to follow. Subjects often looked in the general direction of things that they wanted to steer toward, but gaze fell on nearby objects and surfaces more often than on the actual path or hoops. Nevertheless, subjects were able to perform the task successfully, steering at high speeds while remaining on the path, passing through hoops, and avoiding collisions. In conditions that contained hoops, subjects adapted how they approached the most immediate hoop in anticipation of the position of the subsequent hoop. Taken together, these findings challenge existing models of steering that assume that steering is tightly coupled to where actors look. We consider the study's broader implications as well as limitations, including the focus on a small sample of highly skilled subjects and inherent noise in measurement of gaze direction.

Aviation Collision Risk Warning for Non-Tower Airport Based on Trajectory Prediction

With the development of the civil aviation industry, the throughput of airports continues to increase, including non-tower airports (small airports without the ability to actively communicate with aircraft). These non-tower civil airports can handle small aircraft such as personal aircraft well and lay a solid foundation for future automated aircraft. However, there is a high collision risk in taking off or landing because the probability of avoiding risks highly relies on the pilot’s flight proficiency and experience. To address this problem, we studied the collision risk of aircraft in non-tower airports and proposed a collision risk warning method based on trajectory prediction. The proposed method obtains the probability of future collisions by learning the flight patterns of all aircraft in non-tower airports, the impact of environmental wind speed on pilot behavior, and the interaction patterns between aircraft. At the same time, we conducted in-depth optimization of the probability integration calculation process under a conditional threshold to reduce the time complexity greatly. The simulation experiment results on non-tower airports show that the designed method for collision risk warning can effectively address the aircraft collision prediction problem in non-tower airports, and outperform other benchmark learning methods. It has significant guiding implications for improving airport intelligence and aircraft automation in the future.

Breathalyzer Test & Its Importance in Aviation Industry

The Indian aviation industry has witnessed significant growth in recent years, making safety and regulatory compliance paramount. To maintain the highest standards of safety, the Directorate General of Civil Aviation (DGCA) has implemented rigorous measures, including breathalyser testing for pilots and crew members. This abstract explores the significance of breathalyser testing in the Indian aviation sector, focusing on its role in enhancing safety and ensuring compliance with international aviation standards. Breathalyser testing has become a crucial component of the DGCA's safety protocols, aiming to prevent pilots and crew from operating aircraft under the influence of alcohol. This paper examines the key elements of the DGCA's alcohol testing regulations, including permissible blood alcohol concentration (BAC) limits, testing procedures, prohibition periods, and penalties for violations. The strict adherence to these regulations is essential to guarantee the safety of air travel and to maintain public trust in the industry. The abstract also delves into the impact of breathalyser testing on pilot behaviour and the overall safety culture within the Indian aviation sector. By acting as a strong deterrent against alcohol consumption before or during duty, these tests contribute to a safer and more responsible aviation workforce. Furthermore, this abstract discusses the role of breathalyser testing in aligning the Indian aviation industry with international standards, particularly those set by the International Civil Aviation Organization (ICAO). The adoption of stringent testing procedures demonstrates India's commitment to upholding global aviation safety norms. Finally, this paper highlights the importance of continuous monitoring and improvement of breathalyser testing programs, including pilot education and rehabilitation efforts for those facing alcohol-related issues. Such initiatives not only enhance safety but also promote a culture of responsibility and accountability within the industry. In conclusion, breathalyser testing has emerged as a pivotal tool for enhancing safety and compliance in the Indian aviation industry. It not only aligns with international standards but also plays a vital role in fostering a culture of safety and responsibility among aviation personnel. As the industry continues to expand, the strict enforcement of alcohol testing regulations remains essential to safeguarding the integrity of Indian aviation and ensuring the well-being of passengers and crew.

Mental workload evaluation model of receiver aircraft pilots based on multiple resource theory

AbstractAerial refueling is an extender of air combat capability that has received widespread attention with the development of the military field, particularly the mental workload of the pilot performing the aerial refueling task, as it is the key to the aerial refueling success rate. Therefore, this study analyzes the behavior of receiver aircraft pilots from rendezvous to the separation phase and constructs a mental workload model to improve the aerial refueling success rate. First, the time‐channel‐action unit network was constructed based on Petri net and multiple resource theory (MRT). Second, the mental workload evaluation model was constructed from three dimensions: conventional resource occupation, additional resource occupation, and time occupation. Finally, a simulated experiment was conducted, and the results showed that the mental workload obtained by the model constructed in this study exhibits a high positive correlation with subjective mental workload, pupil diameter, and blink rate, surpassing the accuracy of the traditional MRT model.

Predicting pilot behavior during midair encounters using long short-term memory network

Characterized by the wide use of advanced automation and the introduction of new operation concepts, the future air transportation system will be more complex. Advanced pilot behavior models with improved capability are required to support the design and analysis of the midair encounter situations in the future air transportation system. This paper first filters midair encounter data from Automatic Dependent Surveillance-Broadcast (ADS-B) observations. Based on the acquired midair encounter data, a comprehensive pilot behavior model is proposed based on a multi-layer Long Short-Term Memory (LSTM) network. The model is designed for the purpose of enhancing the predicting capability of pilot behaviors in both horizontal and vertical planes. Finally, the performance of the proposed model to predict pilot behavior in both horizontal and vertical planes is studied through evaluating against realistic midair encounter situations.

Modification of the Pilot Behavior Structural Model and Its Application to the Task of Selecting the Characteristics and Type of Inceptors

Modification of the Pilot Behavior Structural Model and Its Application to the Task of Selecting the Characteristics and Type of Inceptors

Model of the software and hardware complex of automated flight instruction for cadets based on the reference flight pattern

Information is provided regarding the developed model of the software-hardware complex of the automated flight instruction of cadets based on the reference image of the flight. It is proposed to model the movement of a helicopter between two support points as the movement of a material point that has mass and is affected by gravity. The movement model is represented by a system of differential equations of the first order, which describes the movement parameters: air speed, angle of inclination of the trajectory, flight height and distance of movement. Based on the existing regulatory rules for the operation of aircraft, a system of restrictions on movement parameters has been defined. The set of basic flight control bodies is substantiated, the laws of pilot control and restrictions on control parameters, which are also determined by the rules of helicopter operation, are set. The task of finding optimal control parameters at the stage of acceleration of a helicopter at a constant altitude has been formalized. The general structure of the adaptive search system has been built. Procedures for finding optimal parameters have been created using a combined method of finding optimal control parameters based on single-criteria conditional and vector optimization methods. Based on the model experiment, the optimal value of the parity criterion in the method of adaptive convolution of the optimal criterion vector is determined. The calculated data of the proposed model made it possible to find in real time the parameters of the movement of the helicopter under the control of the pilot, the history of the deviation of this movement from the ideal, and the dynamic forecast of such deviation. The possibility of obtaining predicted solutions in the case of existing linear control laws or approximate to linear control laws has been established experimentally. The parameters of the model were determined experimentally. A system of commands of an electronic instructor in verbal form is proposed for correcting the pilot's behavior and bringing his actions closer to optimal ones. Recommendations regarding the practical application of the developed model are substantiated.

พฤติกรรมฝ่าฝืนกฎระเบียบของนักบินที่ส่งผลให้อากาศยานเกิดอุบัติเหตุ

This academic article focus on the behavior modulation violates the pilot regulations. As a guideline and suggestion for reducing the number of accidents caused by pilots not following the regulations. At present, traveling by aircraft has become popular because it is convenient, fast, and time-saving. As a result, there are many people who want to travel by aircraft and air cargo. For this reason, each accident can cost a lot of damage to the aviation industry and related persons. But most accidents are caused by pilots who violate regulations such as flying without following the correct process, drink alcohol before flying and solving problems on their own without listening to the opinion of the crew or air traffic controller. As a result, the situation can't be solved in time and cause an accident. According to the study, make it found the method adjusted behavior in violation of pilot regulations using the flooding technique, It is the most appropriate. As it is a technique that can be applied to the flight training that pilots have to do on a regular basis. It is used for aviation industry to correct the behavior of pilots' violation of regulations and increase the confidence of passengers and related persons in the safety.

Aggressive Behavior of Short-Finned Pilot Whales Towards Sperm Whales in the Gulf of California: Insight into Food Competition

Aggressive Behavior of Short-Finned Pilot Whales Towards Sperm Whales in the Gulf of California: Insight into Food Competition

Takeoff runway overrun risk assessment in aviation safety based on human pilot behavioral characteristics from real flight data

Aviation safety is an important part of safety science and pilot behavior is a major factor in flight safety analysis. However, when analyzing unsafe events, such as runway overrun, majority of studies do not quantify the impact of pilot behavior on flight safety. The aim of this paper is to discover the pilot behavioral characteristics from actual flight data and apply them to construct a takeoff runway overrun risk assessment model. Concretely, the time series clustering is used to mine the pilot behavioral characteristics based on pilot operating data. Then, the similarity theory is introduced to construct a data-based man–machine-environment system expression including pilot behavioral characteristics to get the takeoff distance for the runway overrun risk assessment. The results of case study showed that 3 types of pilot behavioral characteristics were found in selected fleet, which mainly differ in the pilot reaction time after VR and operation input speed of the control column, and RO is more likely to happen under the combined influence of longer reaction time and slower operation input speed. The proposed method makes full use of real flight data from airlines under different conditions incorporating pilot behavior characteristics and can inform risk analysis of other unsafe events such as hard landing, tail strike, etc.

The Effect of the Flight Experience on the Visual Behavior of Pilots – A Preliminary Study

Abstract The eye tracking technique is increasingly used in the context of examining the method of processing visual information from instruments and displays located in the cockpit of an airplane. An important aspect is monitoring the pilot’s visual behavior in the most difficult phase of the flight, which is the landing of the plane. Six people participated in the research, divided into three groups according to their experience. The subjects performed three landing approaches in various weather conditions. During the study, the visual behavior of the participants was recorded using the Pupil Invisible eye tracker. Based on the analysis of heatmaps and areas of interest, differences in the distribution and number of fixations in the visual field between pilots with different aviation experience were shown.

Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism

The abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This article proposes a method of abnormal pilot driving behavior detection based on the improved YOLOv4 deep learning algorithm and by integrating an attention mechanism. Firstly, the semantic image features are extracted by running the deep neural network structure to complete the image and video recognition of pilot driving behavior. Secondly, the CBAM attention mechanism is introduced into the neural network to solve the problem of gradient disappearance during training. The CBAM mechanism includes both channel and spatial attention processes, meaning the feature extraction capability of the network can be improved. Finally, the features are extracted through the convolutional neural network to monitor the abnormal driving behavior of pilots and for example verification. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots during the flight maneuvering phase. The experimental results show that the improved YOLOv4 recognition rate is significantly higher than the unimproved algorithm, and the calling phase has a mAP of 87.35%, an accuracy of 75.76%, and a recall of 87.36%. The smoking phase has a mAP of 87.35%, an accuracy of 85.54%, and a recall of 85.54%. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots in the flight maneuvering phase. This method can quickly and accurately identify the abnormal behavior of pilots, providing an important theoretical reference for abnormal behavior detection and risk management.

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.

With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.

Study of the deformation models of a pilot’s emotional experience in abnormal in-flight circumstances

Aim. An important aspect of flight safety is associated with obtaining information on the potential performance of a pilot in emergency situations. For that purpose, the authors tested a number of currently existing deformation models of a pilot’s emotional experience in terms of the agreement between the predicted level of deformation obtained using such models and the actual level of deformation in abnormal in-flight circumstances (as the pilot’s emotional experience is what resists the destructive action of psychological stress that occurs in abnormal in-flight circumstances). Methods. The conducted study used the following methods: interview, psychological diagnostics, mathematical simulation, expert evaluation. Professional pilots (10 people) who have a level of experience sufficient for this research were interviewed in free form on their personal involvement in abnormal situations that is later evaluated by experts in terms of deformation of emotional experience. Using a special scale, the test subjects evaluated the hazard of their respective abnormal situation, as well as estimated 18 abnormal in-flight situations out of the database collected by the Saint Petersburg State University of Civil Aviation. All study participants were also tested for neuroticism using the Eysenck Personality Inventory (EPI) (adapted by A.G. Shmeliov). Using that data and models in the form of disaster variety, the predicted deformation of the emotional experience was calculated and compared with the expert estimate. Results. While, formally, one of the examined models (A.O. Lengarov’s model) proved to be somewhat superior as compared to the other one (S.G. Lobar’s model), but no fundamental difference between them was identified. The predictive values of emotional experience deformation loosely and insignificantly correlate ( p > 0.1) with the expert estimate. For A.O. Lengarov’s model r corr. = 0.2678, while for S.G. Lobar’s model r corr. = 0.2199. Conclusions. Currently, there is no proven, reliable model of emotional experience deformation. It is required to further collect, analyse and generalise data on pilot behaviour in abnormal in-flight circumstances. This study confirmed the presence of at least two out of eight “catastrophe flags” according to Rene Thom. Thus, the hypothesis that the model of a pilot’s emotional experience deformation is to have the form of fold catastrophe is correct. The method of objectification of abnormal in-flight circumstance hazard evaluation is to be significantly improved, probably, with the use of polynomial approximations, as well as by expanding the abnormal circumstance database of the Saint Petersburg State University of Civil Aviation, primarily by collecting situations involving more recent types of aircraft.

Modeling Fuzzy and Adaptive Human Behavior for Aircraft with Dynamic-Pitch-Control Envelope Cue

As one of the key issues in aviation safety, loss-of-control in the form of adverse aircraft-pilot couplings is attracting attention increasingly. Dynamic-pitch-control envelope shows to be a promising means to evaluate the loss-of-control related to pilot-induced oscillations. To mitigate this issue, this paper develops a human pilot model with the dynamic-pitch-control envelope cue. A key feature of the model is the capability to afford the characteristics of the pilot’s behavior through analyzing the cue of envelope boundaries in different areas. The fuzziness and adaption of the human are introduced into the model to describe the behavior of the human pilot. Fuzzy control logic is designed to reflect the fuzziness of the human’s response to the envelope cue. Time-varying parameters are adjusted to embody the adaptive characteristics of the human pilot to different regional envelope cues. Furthermore, three metrics methods, including error metric, envelope boundaries metric, and scalogram-based pilot-induced oscillation (PIO) metric, are proposed to design the dynamic-pitch-control envelope cues. The assessment results obtained by pilot–aircraft system simulation are compared with the pilot-in-the-loop flight experiment in-ground simulator to validate the effectiveness of the model. Simulation and experimental results show that the proposed human pilot model and envelope cue method can be applied to mitigate the loss-of-control events caused by the pilot–aircraft system oscillations.