Aircraft Conflict Detection Research Articles

Scheduling optimization of continuous climb and descent operations in busy terminal airspace

Based on the current terminal airspace structure, a method for scheduling aircraft arrival and departure that integrates trajectory optimization, conflict detection and multi-objective optimization is proposed to help implement continuous climb and descent operations in busy terminal airspace. First, based on the multi-stage optimal control theory, the Gaussian pseudo-spectral method is used to propose a vertical profile optimization method for continuous climb and descent operations, and the trajectory optimization of continuous climb and descent operations based on the cost index is realized. Secondly, according to the wake turbulence interval and release interval used by the runway, and the horizontal and vertical intervals of air operations, the Mahalanobis distance is used to establish an aircraft conflict detection model. Then, considering the demands of air traffic control, airlines, airports and other operating units, a multi-objective scheduling model and method for aircraft arrival and departure that can achieve the optimization results are proposed. Finally, two sets of arrival and departure data at Guangzhou Baiyun Airport during busy periods are selected, multiple interval parameters are set, alternative paths are introduced, and case analysis and comparative studies are carried out. The results show that during the busy period dominated by departures, the terminal airspace of Guangzhou Baiyun Airport can achieve continuous climb and descent operations during busy periods; during the busy period dominated by arrivals, two aircraft could not be dispatched. The introduction of alternative paths can reduce the number of aircraft that cannot be dispatched.

Operator selection for human-automation teaming: The role of manual task skill in predicting automation failure intervention

Humans working in modern work systems are increasingly required to supervise task automation. We examined whether manual aircraft conflict detection skill predicted participants’ ability to respond to conflict detection automation failures in simulated air traffic control. In a conflict discrimination task (to assess manual skill), participants determined whether pairs of aircraft were in conflict or not by judging their relative-arrival time at common intersection points. Then in a simulated air traffic control task, participants supervised automation which either partially or fully detected and resolved conflicts on their behalf. Automation supervision required participants to detect when automation may have failed and effectively intervene. When automation failed, participants who had better manual conflict detection skill were faster and more accurate to intervene. However, a substantial proportion of variance in failure intervention was not explained by manual conflict detection skill, potentially reflecting that future research should consider other cognitive skills underlying automation supervision.

An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research.

Dual-Horizon Reciprocal Collision Avoidance for Aircraft and Unmanned Aerial Systems

The aircraft conflict detection and resolution problem has been addressed with a wide range of centralised methods in the past few decades, e.g. constraint programming, mathematical programming or metaheuristics. In the context of autonomous, decentralized collision avoidance without explicit coordination, geometric methods provide an elegant, cost-effective approach to avoid collisions between mobile agents, provided they all share a same logic and a same view of the traffic. The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is a state-of-the art geometric method for robot collision avoidance, which can be used as a Detect & Avoid logic on-board aircraft or Unmanned Aerial Vehicles. However, ORCA does not handle well some degenerate situations where agents operate at constant or near-constant speeds, which is a widespread feature of commercial aircraft or fixed-winged Unmanned Airborne Systems. In such degenerate situations, pairs of aircraft could end up flying parallel tracks without ever crossing paths to reach their respective destination. The Constant Speed ORCA (CS-ORCA) was proposed in 2018 to better handle these situations. In this paper, we discuss the limitations of both ORCA and CS-ORCA, and introduce the Dual-Horizon ORCA (DH-ORCA) algorithm, where two time horizons are used respectively for short-term collision avoidance and medium-term path-crossing. We show that this new approach mitigates the main issues of ORCA and CS-ORCA and yields better performances with dense traffic scenarios.

Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

AbstractApron safety is critical to aviation operations and aprons are prone to wingtip scraping accidents. However, few studies have been conducted on wingtip conflicts. In this paper, a class of aircraft conflict detection models based on wingtip keypoint detection is devised to solve the problem of wingtip scraping that may occur during the aircraft taxi‐in process on the apron. Firstly, a concise camera calibration technique is designed for the conversion between the pixel coordinate and the airport actual coordinate, which allows for the acquisition of high accuracy activity posture within the apron. Secondly, the static and dynamic safety protection zone delineation methods for wingtip conflict detection are given, taking the apron standard operating procedures and aircraft kinematic model into consideration. Then, the corresponding conflict detection models and algorithms are designed for the possible scraping between the vehicle and aircraft wingtips and between the aircraft and aircraft wingtips during the aircraft taxi‐in process on the apron. Finally, HRNet is selected to detect the keypoint of the wingtip by comparison, and the corresponding models and algorithm validation are carried out based on the apron physical sandbox. The verification results indicate that the models have strong accuracy and real‐time performance.

Uçak Çakışma Saptama Ve Çözümleme Problemi için Karma Tam Sayılı Doğrusal Programlama Modeli Yaklaşımı

An increasing demand in air transportation leads to a significant rise in traffic density both at airspaces and at airports. This rise in air traffic density may cause both delays and conflicts between aircraft pairs. Aircraft conflict detection and resolution problem is discussed in this study and a mixed integer linear programming model is proposed for this problem. A baseline case was generated to test the proposed mathematical model. The baseline case decides which aircraft receive delay for the resolution of conflicts, with the principle of the first come first served. The proposed model minimizes total delay time and total fuel consumption due to delay. These objectives were compared with the baseline considering the functions both individually and together. According to the results, the enhancement of the total fuel consumption due to delay and total delay duration are 17.3% and 3.2% compared to the baseline cases. When the two functions are considered together, 15.2% reduction in fuel consumption was obtained.

Probabilistic multi-aircraft conflict detection and resolution considering wind forecast uncertainty

A probabilistic method for aircraft conflict detection and resolution considering the effects of wind forecast uncertainty is presented in this paper. The wind components are modeled as random variables, described by a joint probability density function. The probabilistic conflict detection problem is tackled using the Probabilistic Transformation Method. Two conflict indicators are used to characterize a conflict between a pair of aircraft: the distance of closest approach (which is uncertain) and the probability of conflict. The probabilistic conflict resolution consists in modifying the aircraft trajectories so that the probability of conflict between any pair of aircraft be less than a predefined safety threshold; this problem is formulated as a nonlinear programming problem subject to constraints, where the optimality criterion is the minimization of the deviation of the aircraft resolution trajectories from their nominal trajectories. The case of multiple en-route aircraft flying with constant airspeed and flight level is considered, where they follow approaching multi-segment trajectories and are affected by the same uncertain wind. The wind components are obtained from Ensemble Weather Forecasts. Numerical results are presented for a particular application where the wind components follow four-parameter beta distributions. The cost of the resolution process is analyzed, considering both cooperative and non-cooperative scenarios.

A Data-driven Framework for Long-Range Aircraft Conflict Detection and Resolution

At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel Data-driven Framework to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the framework declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The framework resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the framework learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the framework avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict free. The key concept upon which the framework is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our framework using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.

Multiple Model Method for Aircraft Conflict Detection and Resolution in Intent and Weather Uncertainty

This paper presents a novel multiple model (MM) method for aircraft conflict detection and resolution (CDR) in intent and weather uncertainty for the next generation air transportation system. It is based on probabilistic MM aircraft trajectory prediction. Conflict detection is performed through the use of predicted probability of conflict (PC). An improved algorithm for estimating PC is proposed and compared with a commonly used method. Conflict resolution (CR) is formulated as a stochastic model predictive control (MPC) problem subject to a constraint on the predicted PC to guarantee safety of the optimized CR trajectories. The cost function to be minimized is tailored to the CR problem and includes the cost of performing evasive maneuvers as well as the average cost incurred by extra travel distance to the destination due to the maneuvers. An efficient algorithm for finding the optimal path (maneuver sequence), with respect to the maneuver cost, is proposed and extended to an overall CDR optimization algorithm for the total cost, in order to produce optimized conflict-free CR trajectories. The proposed search algorithm is essentially a generalization of an optimal list Viterbi algorithm (for unconstrained minimization through a trellis) to the constrained case of our MPC-based CDR problem. The capability and computational efficiency of the proposed MM-CDR method (including PC prediction, optimal search, and overall CDR) are evaluated via comprehensive simulation of a large variety of unmanned aerial vehicle “sense-and-avoid” encounter scenarios, and are compared with existing methods in the literature.

A Methodology to Perform Air Traffic Complexity Analysis Based on Spatio-Temporal Regions Constructed Around Aircraft Conflicts

One of the main missions of air traffic management is to guarantee en route safety. This safety is quantified through some minimum separation distance between pairs of flying aircraft. Current systems are human-based, i.e., have human air traffic controllers assuring minimum separation is maintained and in cases a loss of separation is predicted, they take actions to prevent the occurrence of such events. The constant and rapid increment of the air traffic demand is pushing current air traffic control systems to their limits. Development of automatic decision support systems, which can be used to automate, or support aircraft conflict detection and resolution, is considered a possible solution. However, the combinatorial nature of the problem poses several challenges for such a task. Metrics or various analytical procedures to produce information about the complexity of given scenarios can be used to guide the solution search process. In this paper, we present a complexity analysis based on the spatio-temporal interdependencies identified by the use of spatio-temporal regions constructed around the aircraft conflicts.

A SVM Approach of Aircraft Conflict Detection in Free Flight

Probabilistic conflict detection methods typically require high computational burden to deal with complex multiaircraft conflict detection. In this article, aircraft conflict detection is considered as a binary classification problem; therefore, it can be solved by a pattern recognition method. A potential conflict would be identified, as long as its flight data features are extracted and fed to a classifier which has been trained by a large number of flight datasets. Based on this, a new method based on support vector machine (SVM) is employed to detect multiaircraft conflict in “Free Flight” airspace and to estimate the conflict probability. For that purpose, the current positions, velocity vectors, and predicted look-ahead time are selected as detection factors, and the detection model is established by SVM to detect aircraft conflict within look-ahead time during short and medium terms. Moreover, conflict probabilities are determined by the sigmoid function mapping method. Nevertheless, false alarm rate is always a first and foremost problem that troubles air traffic controllers. For the purpose of reducing false alarm rates, Synthetic Minority Over-sampling Technique (SMOTE) method is used to handle imbalanced datasets. Extensive simulation results are presented to illustrate the rationality and accuracy of this method.

Using Eye Movement Data Visualization to Enhance Training of Air Traffic Controllers: A Dynamic Network Approach

The Federal Aviation Administration (FAA) forecasted substantial increase in the US air traffic volume creating a high demand in Air Traffic Control Specialists (ATCSs). Training times and passing rates for ATCSs might be improved if expert ATCSs’ eye movement (EM) characteristics can be utilized to support effective training. However, effective EM visualization is difficult for a dynamic task (e.g. aircraft conflict detection and mitigation) that includes interrogating multi-element targets that are dynamically moving, appearing, disappearing, and overlapping within a display. To address the issues, a dynamic network-based approach is introduced that integrates adapted visualizations (i.e. time-frame networks and normalized dot/bar plots) with measures used in network science (i.e. indegree, closeness, and betweenness) to provide in-depth EM analysis. The proposed approach was applied in an aircraft conflict task using a high-fidelity simulator; employing the use of veteran ATCSs and pseudo pilots. Results show that, ATCSs’ visual attention to multi-element dynamic targets can be effectively interpreted and supported through multiple evidences obtained from the various visualization and associated measures. In addition, we discovered that fewer eye fixation numbers or shorter eye fixation durations on a target may not necessarily indicate the target is less important when analyzing the flow of visual attention within a network. The results show promise in cohesively analyzing and visualizing various eye movement characteristics to better support training.

Exact and Approximate Solving of the Aircraft Collision Resolution Problem via Turn Changes

The aircraft conflict detection and resolution problem in air traffic management consists of deciding the best strategy for an arbitrary aircraft configuration such that all conflicts in the airspace are avoided. A conflict situation occurs if two or more aircraft do not maintain the minimum safety distance during their flight plans. A two-step approach is presented. The first step consists of a nonconvex mixed integer nonlinear optimization (MINLO) model based on geometric constructions. The objective is to minimize the weighted aircraft angle variations to obtain the new flight configuration. The second step consists of a set of unconstrained quadratic optimization models where aircraft are forced to return to their original flight plan as soon as possible once there is no aircraft in conflict with any other. The main results of extensive computation are reported by comparing the performance of state-of-the-art nonconvex MINLO solvers and an approximation by discretizing the possible angles of motion for solving a sequence of integer linear optimization (SILO) models in an iterative way. Minotaur, one of the nonconvex MINLO solvers experimented with, gives better solutions but requires more computation time than the SILO approach, which requires only a short time to obtain a good, feasible solution. Its value in the objective function has a reasonable goodness gap compared with the Minotaur solution. Given the need to solve the problem in almost real time, the approximate SILO approach is favored because of its short computation time and solution quality for the testbeds used in the experiment, which include both small- and real-sized instances. However, Minotaur is useful in this particular case for simulation purposes and for calibrating the SILO approach.

Shared Mental Models in Human-Machine Systems

Abstract: Imagine a future where humans and machines are able to share tasks, to monitor each other’s performance, and to interchange (control) authority whenever required or desired. In aviation, this vision was conceptualized almost twenty-five years ago by the late Charles Billings and is formally known as Human-Centered Automation. Although the aviation community has embraced this perspective, it proves to be difficult to realize this envisioned level of human-machine collaboration, especially for cognitive tasks. To achieve a breakthrough, I argue that we first have to consider what seems to be missing from current forms of automation that is fundamental to effective inter-human collaboration: the possibility to share mental models (or representations) of the problem(s) to solve. When looking at human-human interaction, productive team thinking and problem-solving efforts are accomplished when teammates have a “common ground” or shared understanding of the work to be done and the various ways to do it. Similarly, when work would be distributed over human and automated agents, the constraints introduced by the other agents are properties of the work domain and must therefore be shared. But how can more information be shared while not overloading the human’s capacity to learn and solve problems? In this paper I argue that the Ecological Interface Design paradigm can provide the means and guidelines to pursue shared human-automation mental models that will facilitate productive thinking. I will illustrate this by means of an example in the field of aircraft conflict detection and resolution.

An exact multi-objective mixed integer nonlinear optimization approach for aircraft conflict resolution

An exact mixed integer nonlinear optimization (MINO) model is presented for tackling the aircraft conflict detection and resolution problem in air traffic management. Given a set of flights and their configurations, the aim of the problem was to provide new configurations such that all conflict situations are avoided, with conflict situation understood to mean an event in which two or more aircraft violate the minimum safety distances that must be maintained in flight. The proposed model solves the problem using both horizontal (velocity and angle turn change) and vertical (altitude level change) maneuvers. As a special case, another model is presented in which only horizontal maneuvers are used. The models proposed are based on a geometric construction which involves trigonometric functions, so the constraint system is included via a large set of trigonometric and nonconvex inequalities. A multicriteria approach is presented to provide useful information to air traffic control officers about the maneuvers to be performed. The main results of an extensive computational experiment are reported in which the performance of the state-of-the-art nonconvex MINO solver Minotaur is studied.

Stochastic optimal control for aircraft conflict resolution under wind uncertainty

In this paper, a stochastic optimal control method is developed for determining three-dimensional conflict-free aircraft trajectories under wind uncertainty. First, a spatially correlated wind model is used to describe the wind uncertainty, and a probabilistic conflict detection algorithm using the generalized polynomial chaos method is proposed. The generalized polynomial chaos algorithm can quantify uncertainties in complex nonlinear dynamical systems with high computational efficiency. In addition, a numerical algorithm that incorporates the generalized polynomial chaos method into the pseudospectral method is proposed to solve the conflict resolution problem as the stochastic optimal control problem. The stochastic optimal control method is combined with the proposed conflict detection algorithm to solve the conflict resolution problem under the wind uncertainty. Through illustrative three-dimensional aircraft conflict detection and resolution examples with multiple heterogeneous aircraft, the performance and effectiveness of the proposed conflict detection and resolution algorithms are evaluated and demonstrated.

An Eye Movement Analysis Algorithm for a Multielement Target Tracking Task: Maximum Transition-Based Agglomerative Hierarchical Clustering

An algorithm was developed to characterize, compare, and analyze eye movement sequences that occur during visual tracking of multiple moving targets. When individuals perform a task requiring interrogating multiple moving targets, complex and long eye movement sequences occur, making sequence comparisons difficult in whole and in part. The developed algorithm characterizes a sequence by hierarchically clustering the targets that an individual interrogated through an unordered transition matrix created from the frequencies of eye fixation transitions among the targets. Then, the resulting sets of clustered targets, which we define as multilevel visual groupings (VGs), can be compared with analyze performance. The algorithm was applied to an aircraft conflict detection task. Eye movement data were collected from 25 expert air traffic controllers and 40 novices. The task was to detect air traffic conflicts for easy, moderate, and hard difficulty scenarios on simulated radar display. Experts' and novices' multilevel (level one composed of pairs, and level two composed of three or four targets) VGs were aggregated and visualized. Chisquare tests confirmed that there were significant differences for easy (level one: p <; 0.001, level two: p = 0.004), moderate (level two: p = 0.047), and hard (level two: p <; 0.001) difficulty scenarios. The algorithm supported identifying different eye movement characteristics between experts and novices. Scans of the experts had multilevel VGs around the conflict pairs, whereas those of the novices included different aircraft. The results show promise for using the compact representation of eye movements for performance analysis.

Using scanpaths as a learning method for a conflict detection task of multiple target tracking.

The objective was to determine whether the scanpaths of air traffic controllers (ATCs) could be used to improve the performance of novices in a conflict detection task. Studies in other domains show that novice performance can be improved by exposure to experts' scanpaths. Whether this effect can be found for an aircraft conflict detection task is unknown. Scanpaths of 25 professional ATCs ("experts") were recorded using a medium-fidelity air traffic control simulation with realistic scripted traffic that included aircraft pairs that would lose separation. A total of 20 novices were exposed to experts' scanpaths ("treatment"), and their performance (for both loss of separation detection rates and false alarm rates) was compared to that of 20 novices given no treatment or instructions ("control") and 20 novices who were verbally instructed to attend to altitude ("instruction-only"). Interviews were held about the helpfulness of the exposure. The scanpaths were analyzed to find pattern differences among the three groups. Chi-square tests showed significant differences for false alarm rates across the three groups (p = .001). Pairwise Mann-Whitney tests showed that the number of false alarms for the treatment group was significantly lower than that for the control group (p = .005), and trended lower than the instruction-only group (p = .08). Treatment group participants responded that experts' scanpaths helped. Analysis of scanpaths showed an increased tendency of the scanpath treatment group to follow the experts' scanpath. The scanpath training intervention improved novice performance by reducing false alarms. Implementing experts' scanpaths into novices' active learning process shows promise in enhancing training effectiveness and reducing training time.

A VNS metaheuristic for solving the aircraft conflict detection and resolution problem by performing turn changes

The aircraft Conflict Detection and Resolution (CDR) problem in air traffic management consists of finding a new configuration for a set of aircraft such that conflict situations between them are avoided. A conflict situation arises if two or more aircraft violate the safety distances that they must maintain in flight. In this paper we propose a Variable Neighborhood Search approach for solving the CDR by turn changes. This metaheuristic compares favorably with previous best known methods for solving the Mixed Integer Nonlinear Programming (MINLP) model proposed elsewhere. It is worth pointing out the astonishingly short time in which the first feasible solution is obtained. This is crucial for this specific problem, where a response must be provided almost in real time if it is to be useful in a real-life problem. A comparative study between the performance of the new approach, a state-of-the-art MINLP solver and our Sequential Integer Linear Optimization approach proposed elsewhere is reported, using a testbed of instances with up to 25 aircraft.

Top-Down Approach for a Linguistic Fuzzy Logic Model

A top-down approach is introduced for processing nonlinear linguistic fuzzy logic within the Mamdani model itself in order to reduce the computational efforts. The top-down approach is composed of three steps: (1) directly obtain the best set of linguistic terms, (2) find the matching rule antecedents and their output values, and (3) visit the neighbor terms. A numerical example is provided along with detailed analysis of the approach. In addition, the approach is applied to a simplified aircraft conflict detection task. The results show that the top-down approach is effective at reducing the amount of computation compared to the classical search approach.