A methodology for estimating the probability of potential secondary conflicts

4D-trajectory based operations is one of the big enabler for future high-capacity, efficient and environmentally friendly air traffic management. Every aircraft is scheduled to fly along a predicted 4D path that can be calculated from gate to gate pre-flight. 4D trajectories are optimized individually for aircraft taking into account performance models, routes, weather conditions and airline preferences. However, individual calculation of trajectories does not ensure conflict-freeness with surrounding traffic. This work describes an efficient algorithm detecting conflicts for large traffic scenarios. Conflict detection is performed between aircraft trajectories, also taking into account environmental constraints like severe weather zones and restricted areas. Basic idea is an N-dimensional bisection of airspace allowing a significant reduction of complexity. Thus, potential conflicts are identified very fast. A slower high precision conflict check is performed on potential conflicts only. On average, conflicts of one 4D-trajectory can be detected in a European traffic sample holding more than 33 000 flights in less than 2.5 ms on standard PC hardware. Fast detection times are predestined for trial-and-error conflict resolution. Different conflict resolution methods are illustrated, taking into account the major key performance areas in air traffic management, e. g., safety, efficiency, and predictability. As an example, deconfliction is performed on an optimized version of aforementioned European traffic sample holding 33 000 flights.

Conflict detection and resolution is one of the most important tasks in daily railway traffic management, although it is still difficult to solve all its aspects. In fact, the aspect of conflict detection has not been amply studied. In this paper, an approach of traffic state prediction and conflict detection, based on proper state transition maps (STMaps) and corresponding relation matrices, is proposed. First, the traffic state sequences, which mainly concern infrastructure status and train movement information, are studied. These state sequences are expressed as segment and route state vectors and kept in corresponding state-domain tables (SDTables). The empirical state transitions are then applied to detect irregular states in a dynamic traffic environment. Furthermore, the structural constraints of infrastructure topology and route compatibilities are represented in matrices to aid the calculation and prediction of potential conflicting situations. Scenarios such as train delay and infrastructure failure are designed to test the proposed approach. The test results show that irregular states can be efficiently detected and potential conflicts can be further identified, and the detailed conflict information is also approachable.

With the rapid development of the aviation industry, the complexity and intertwinement of various aviation activities have been continuously increasing, leading to the escalating of potential conflicts in limited airspace resources. Efficient airspace management is crucial for flight safety. To address the issues of conflict detection and resolution in localized high-density multi-airspace environments, this paper proposes an end-to-end integrated framework that integrates octree spatial partitioning, hierarchical conflict detection, and a conflict resolution module based on the Improved Whale Migration Algorithm (IWMA). This framework takes the airspace safety separation standards as constraints and the minimization of multi-airspace adjustment offsets as the optimization objective. The IWMA integrates diverse population structures and multi-strategy optimization technologies to balance global search and local exploration capabilities and avoid falling into local optima. Experiments on high-density conflict scenarios of different scales have verified the effectiveness and robustness of the proposed method. Simulation results show that compared with traditional detection methods and other classic optimization algorithms, the conflict detection and resolution method proposed in this paper can quickly and efficiently provide reliable solutions for multi-airspace conflict issues.

Cooperation of combinatorial solvers for en-route conflict resolution

Conflict detection algorithms can be broadly classified as state based and intent based algorithms. State based algorithms predict the path of aircraft by projecting their current position and velocity vectors. The path predictions are then used to determine if the aircraft are in conflict within a look ahead time. That is, if loss of separation occurs in the future. Intent based algorithm uses flight plans and other information, which usually resides in a flight management computer, to predict the path of aircraft. Algorithms that use intent information for conflict detection obtain the intent of other aircraft and broadcast their own intent via some type of communication link. Intent information that is exchanged among the aircraft is defined as explicit intent. A conflict detection algorithm can also make use of intent information based on aircraft nominal routes, corridors, published approaches, etc., and in this case, there is no information exchange between aircraft. This kind of intent is defined as implicit intent. Implicit intent can be used very effectively in conflict detection without the added cost and complexity of communication links. The use of implicit intent, in combination with state information, reduces false alarms over a state based conflict detection algorithm and therefore increases the effectiveness of the alerting system. In this paper, conflict detection using implicit intent information is described and its performance is compared with a state based conflict detection algorithm.

To predict the potential conflict during the flight routes, a conflict detection approach based on multi-waypoints is presented. Aimed at the encounter geometry of two aircrafts with multi-waypoints, in order to simply the computational process, the aircrafts routes are divided into several straight conflict paths. Model along track kinematics equation of the aircraft by referencing to a classical conclusion that the along track error of the aircraft is normal distributed, consider an encounter between two aircrafts at the same flight level , construct an ellipse conflict region, by establishing a rectangular coordinate system represented aircraft position appropriately . First, propose a probability estimation algorithm of medium term conflict detection for cross routes at the same level, then analyze the relationship between the conflict probability and several factors based on various graphs. MATLAB result shows that the proposed algorithm is advanced in its simple calculation method, fast calculation speed and precise detection calculations.

Stochastic optimal control for aircraft conflict resolution under wind uncertainty

Prevention and Control of Seasonal Influenza With Vaccines: Recommendations of the Advisory Committee on Immunization Practices (ACIP)—United States, 2014–15 Influenza Season

A method for conflict detection and resolution based on the parameterization of the aircraft intents is presented. The method is also based on the use of predefined trajectory patterns, which are in fact flight intents; these patterns include standard airline procedures and air traffic control regulations. The conflict resolution problem is formulated as a parametric optimization problem subject to constraints; the optimality criterium is defined so that the deviation from the intended (preferred) trajectories in the lateral profile is minimized. The resolution trajectory patterns take into account changes of the nominal waypoints (vectoring) and changes of the aircraft speeds. The method is applied to the case of multiple conflicts among commercial transport aircraft in converging traffic in the terminal area; different scenarios are considered, which include locked aircraft, that is, aircraft whose trajectories are known and fixed. The conflict detection and resolution algorithm relies on a kinetic trajectory predictor (nonlinear point-mass model with variable mass), which is accurate, flexible, and transparent, and provides the high-fidelity prediction required in the analysis of the demanding terminal area traffic. The cost of the global conflict resolution process is assessed, in terms of extra distance traveled, extra flight time, and extra fuel consumed for each aircraft.

Despite the presumed frequency of conflicts of interest in scientific peer review, there is a paucity of data in the literature reporting on the frequency and type of conflicts that occur, particularly with regard to the peer review of basic science applications. To address this gap, the American Institute of Biological Sciences (AIBS) conducted a retrospective analysis of conflict of interest data from the peer review of 282 biomedical research applications via several onsite review panels. The overall conflicted-ness of these panels was significantly lower than that reported for regulatory review. In addition, the majority of identified conflicts were institutional or collaborative in nature. No direct financial conflicts were identified, although this is likely due to the relatively basic science nature of the research. It was also found that 65 % of identified conflicts were manually detected by AIBS staff searching reviewer CVs and application documents, with the remaining 35 % resulting from self-reporting. The lack of self-reporting may be in part attributed to a lack of perceived risk of the conflict. This result indicates that many potential conflicts go unreported in peer review, underscoring the importance of improving detection methods and standardizing the reporting of reviewer and applicant conflict of interest information.

The detection and resolution of conflicts among norms are key processes to guarantee the proper behavior of multi-agent systems (MAS) regulated by norms. A way of regulation is required to restrict and guide the autonomous and possibly heterogeneous software agents that act according to their own interests. When norms are applied to regulate MAS, normative conflicts may arise. A conflict between norms is a situation in which the fulfillment of a norm causes a violation of another one. We present and compare different techniques proposed to detect and resolve conflicts.

This paper deals with conflict detection for Unmanned Aerial Vehicles (UAVs). We provide an exhaustive mathematical framework for 3D conflict detection and propose a novel distributed conflict detection algorithm. The suggested algorithm requires minimal communication. Indeed, only the state information (i.e., position and velocity vectors) is periodically exchanged between the UAVs. We carry out simulations, using the OMNET++ simulator, to evaluate its performance. Simulation results indicate that the proposed algorithm performs well in terms of false alarms, missed alarms and scalability to increased number of UAVs.

This paper introduces a real-time means to detect and avoid conflicts for a conceptual Free Flight environment. The assumption of this paper is approximately the same as the model given by Lee C.Yang of MIT. The concept assumes that the datalinks (e.g. ADS-B IN) between different aircraft are available and that conflicts are primarily resolved on the flight deck. This paper provides the flight crew multiple ways to avoid the conflicts, including turn, acceleration, deceleration, climb and descent. The alerting logic is based on probabilistic analysis through modeling of aircraft sensors and trajectory uncertainties. Monte Carlo simulations are used to determine conflict probability for every possible encounter situation. The construction of conflict probability database (CPDB) before the aircraft takes off can reduce the computation time of the flight computer. The CPDB records the conflict probability corresponding to every possible air traffic situation in which there are only two aircraft. Due to the integrity of the constructed CPDB, CPDB is suitable to be used to study the characters of the air traffic conflict. The Avoidance Maneuver Database (AMDB) and the Redundant Maneuver Database (RMDB) have been constructed based on the CPDB which is established in advance. AMDB and RMDB are used in resolving conflicts in multiple aircraft scenario.

In this paper four conflict prediction approaches are considered: a classical geometric approach, two variations of a probabilistic approach developed by Paielli OC Erzberger, based on conflict probability and overlap probability, and a novel probabilistic approach. The objective of all conflict prediction approaches is to evaluate a set of planned or predicted trajectories on their conflict potential and to supply other Air Traffic Management (ATM) subsystems with the conflict information. The classical geometric approach and approaches based on conflict probability and overlap probability are briefly reviewed. The novel probabilistic approach is described and explained in more detail. Simulation results for ATM examples are provided and compared for the four approaches on flexibility of usage and imposed restrictions on aircraft behaviour.

Conflict Detection and Resolution (CD&R) for Unmanned Aerial Vehicles (UAVs) has become important consideration with remarkable development of sensor technology and autonomous control system. Since CD&R deals with safety of UAVs and aircraft, it has to be validated by rigorous analytical verification. This paper investigates the application of differential geometry to UAV CD&R algorithm for non-cooperating intruders such as static and dynamic obstacles. Two-dimensional geometry is used to detect a single conflict and multiple conflicts, and find the condition for resolution guidance. The existence and stability of the conflict resolution, which controls either heading angle only or heading angle and ground speed simultaneously, are then analysed. Whilst there exist region which the UAV can not resolve due to its physical and operational limitations as every CD&R algorithms, the resolution guidance can find the region and prove local stability using Lyapunov theory. The analysis and resolution guidance design do not rely on local linearisation and can be shown that the derived conflict resolution is equivalent concepts to Proportional Navigation Guidance (PNG) and Traffic alert and Collision Avoidance Systems (TCAS). The paper finishes with numerical simulation in two dimensions, illustrating the performance and properties of the proposed CD&R algorithm.