Conflict Detection Algorithm Research Articles

Modeling contextual goals and detecting context conflicts from BDD-based user stories

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development.

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

AbstractThere is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo‐fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real‐time conflict analysis for a large number of UAVs flying in the aerial space of a city (U‐Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U‐Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.

Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation

With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existing working mode of simply using computer mapping and manually finding airspace conflict contradictions can no longer meet the large-scale air use demand. In response to the existing spatial representation model of longitude and latitude grid, which has large grid deformation in high latitude areas, and the problem of slow computation speed of the conflict detection (CD) algorithm that determines whether the airspace boundary coordinates overlap, we propose a grid model that represents airspace with a spherical rhombic discrete grid of positive icosahedron and design a matrix-based digital representation method of airspace, which uses matrix product operation. The matrix product operation is used to quickly determine whether there is a conflict between airspace and airspace and between airspace and routes.

Predicting sUAS conflicts in the national airspace with interacting multiple models and Haversine-based conflict detection system

In this paper, a conflict detection system for small Unmanned Aerial Vehicles (sUAS), composed of an interacting multiple model state predictor and a Haversine-distance based conflict detector, is proposed. The conflict detection system was developed and tested via a random recursive simulation in the ROS-Gazebo physics engine environment. The simulation consisted of ten small unmanned aerial vehicles flying along randomly assigned way-point navigation missions within a confined airspace. Way-points are generated from a uniform distribution and then sent to each vehicle. The interacting multiple model state predictor runs on a ground-based system and only has access to current vehicle positional information. It does not have access to the future way-points of individual vehicles. The state predictor is based on Kalman filters that utilize constant velocity, constant acceleration, and constant turn models. It generates near-future position estimates for all vehicles operating within an airspace. These models are probabilistically fused together and projected into the near-future to generate state predictions. These state predictions are then passed to the Haversine distance-based conflict detection algorithm to compare state estimates and identify probable conflicts. The conflicts are detected and flagged based on tunable threshold values which compare distances between predictions for the vehicles operating within the airspace. This paper discusses the development of the random recursive simulation for the ROS-Gazebo framework and the derivation of the interacting multiple model along-with the Haversine-based future conflict detector. The results are presented via simulation to highlight mid-air conflict detection application for sUAS operations in the National Airspace.

Global path conflict detection algorithm of multiple agricultural machinery cooperation based on topographic map and time window

Path planning is one of the crucial problems of multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. Studies on global path conflict detection algorithms based on topographic maps and time windows have been conducted to solve the global path conflict problem of multiple agricultural machinery cooperation in the farmland operation environment. First, the global path conflict problem of multiple agricultural machinery cooperation was analyzed. The constraints of time window conflict detection and the time parameters to be considered in calculating the time window were proposed. Then, the global path preplanning was performed based on a topographic map and Dijkstra algorithm. According to the path preplanning results, the global path conflict was detected based on the time window. Finally, the global path conflict detection algorithm was simulated on MATLAB with the topographic map of Zhuozhou Experimental Farm as an example. The global path preplanning results show that the four paths overlapped in different degrees at nodes 1, 2, 7, 11, 15, 20, and 27. Path 3 and Path 1 were inclusive interval-type conflicts, and Path 4 and Path 1 were node-type conflicts. Path 3 adopted the change path strategy after the conflicting paths were replanned. The path was changed from [15-11-7-2-1-5-10-14-18-31] to [15-21-24-18-31]. Path 4 adopted the waiting strategy, and its paths did not change. Although the four paths still had different degrees of path overlap, there were no time conflict in the overlapping part of the replanned path. The simulation results show that the global path optimization and path conflict detection of multiple agricultural machinery cooperation could be realized based on the topographic map and time window. Moreover, the conflict resolution strategy with the least time could be obtained to achieve a safe, efficient, and conflict-free global path of multiple agricultural machinery cooperation. Thus, a foundation for further solving the path planning of multiple agricultural machinery cooperation in the complex operation environment of regional farmland is laid.

Policy conflict detection approach for decision-making in intelligent industrial Internet of Things

Improving Industrial Internet of Things (IIOT), device flexibility and lowering maintenance costs are significant problems. However, as the scale of the Intelligent Industrial Internet of Things (IIIOTs) system expands, the interactions between the rules get more sophisticated, potentially leading to rule discrepancies. The algorithm for Formal Rule Conflict Detection (FRCD) is created, and a thorough explanation of the procedure is given in this paper. Two IIIOT systems were used in experiments and the results were compared with three existing standard IIIOT rule conflict detection techniques. They include policy conflict detection systems based on Web semantics (Semantic Web-Based Policy Interaction Detection with Rules, (SPIDER)), conflict detection methods based on Users, Triggers, Environment entities, and Actuators (UTEA), and semiformal conflict detection methods (Identifying Requirements Interactions using Semiformal (IRIS)). The experimental results show that the FRCD rule conflict detection method is superior.

4D Trajectory Conflict Detection and Resolution Using Decision Tree Pruning Method

The aviation community develops Trajectory Based Operations (TBO) as an advancement in Air Traffic Management (ATM). There is still the need for an efficient scheme to present the trajectories, manage their associated data, and further detect and resolve the conflicts (CDR) that should eventually occur. In this research, we develop a CDR framework for managing predicted 4-Dimensional Trajectory (4DT). Using Not Only SQL (NoSQL) database (Cassandra and MongoDB), the 4D trajectories of related routes are presented, and the possible conflicts are detected using the strategy of Computing in NoSQL Database. Compared with other conflict detection algorithms, usually by the pairwise method with O(n2) at least, the proposed Decision Tree Pruning Method (DTPM) effectively treats massive data sets. The 4DT data are collected by Trajectory Predictor (TP) concerning 58% of the whole Brazilian air traffic. The comparison results between Cassandra and MongoDB from the case studies show the effectiveness of the proposed methods for conflict detection. In addition, we prove that the conflict resolution approach is viable for application in real scenarios, finding near-optimal solutions for the conflicts identified by the framework. Finally, we also demonstrated the development of sustainable artificial intelligence in intelligent air transportation to improve safety in air traffic management.

The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs

Conflict detection is an essential issue in flying ad hoc networks (FANETs) to ensure the safety of unmanned aerial vehicles (UAVs) during flights. This paper assesses the applicability and utilization of a conflict detection algorithm that sees immediate trajectory as a straight line for short periods with nonlinear mobility models such as Gauss–Markov (GM). First, we use a straight line conflict detection algorithm with two nonlinear mobility models. Then, we perform an extensive simulation study to evaluate the performance. Additionally, we present a comprehensive discussion to tune the collision detection parameters efficiently. Simulation results indicate that an algorithm considering the immediate trajectory as a straight line to predict conflicts between UAVs can be applied with nonlinear mobilities and can provide an acceptable performance measured in false and missed alarms.

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.

Intent Based Networking management with conflict detection and policy resolution in an enterprise network

Intent-Based Networking (IBN) is a novel networking paradigm that allows networks to be autonomously configured, continuously assured, and to be highly adaptable to high-level intentions of network users and operators. In IBN systems, conflict detection and policy resolution modules are crucial to intent activation to enforce correct network configurations. To this end, in this study, we propose an extensible intent model, complemented with a conflict detection algorithm. We also propose a policy resolution algorithm that is based on a two-dimensional analysis of intent endpoints and time spans. To better showcase the efficiency of our algorithm, we also developed an enterprise-based IBN intent management web application for network users and administrators. Our evaluation experiments reveal the effectiveness of our algorithms in terms of reliable resolutions and fast response time.

DTC: A Dynamic Transaction Chopping Technique for Geo-Replicated Storage Services

Replicating data across geo-distributed datacenters is usually necessary for large scale cloud services to achieve high locality, durability and availability. One of the major challenges in such geo-replicated data services lies in consistency maintenance, which usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to address this challenge. However, existing chopping is conducted statically during programming, which is stubborn and complex for developers. In this article, we propose Dynamic Transaction Chopping (DTC), a novel technique that does transaction chopping and determines piecewise execution in a dynamic and automatic way. DTC mainly consists of two parts: a dynamic chopper to dynamically divide transactions into pieces according to the data partition scheme, and a conflict detection algorithm to check the safety of the dynamic chopping. Compared with existing techniques, DTC has several advantages: transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to data partition schemes. A prototype of DTC is implemented to verify the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much better performance than similar work.

Flight Conflict Detection Algorithm Based on Relevance Vector Machine

In response to the problems of slow running speed and high error rates of traditional flight conflict detection algorithms, in this paper, we propose a conflict detection algorithm based on the use of a relevance vector machine. A set of symmetrical historical flight data was used as the training set of the model, and we used the SMOTE resampling method to optimize the training set. We obtained relatively symmetrical training data and trained it with the relevance vector machine, improving the kernels through an intelligent algorithm. We tested this method with new symmetrical flight data. The improved algorithm greatly improved the running speed and was able to effectively reduce the missed alarm rate of in-flight conflict detection symmetrically, thus effectively ensuring flight safety.

Fast Conflict Detection for Multi-Dimensional Packet Filters

To support advanced network services, Internet routers must perform packet classification based on a set of rules called packet filters. If two or more filters overlap, a filter conflict will occur and lead to ambiguity in packet classification. Further, it may affect network security or even the correctness of packet routing. Hence, it is necessary to detect conflicts to avoid the above problems. In recent years, many conflict detection algorithms have been proposed, but most of them detect conflicts for only prefix fields (i.e., source/destination IP address fields) of filters. For greater practicality, conflict detection must include non-prefix fields such as source/destination IP port fields and the protocol field. In this study, we propose an efficient conflict detection algorithm for five-dimensional filters, which include both prefix and non-prefix fields. In the proposed algorithm, a tiny lookup table is created for quickly filtering out a large portion of non-conflicting filter pairs, thereby reducing the overall conflict detection time. Experimental results show that our algorithm reduces the detection time by 10% to 28% compared with other conflict detection algorithms for 20 K filter databases. More importantly, our algorithm can be used to extend any existing conflict detection algorithms for two-dimensional filters to support fast conflict detection for five-dimensional filters.

A Fast Route Conflict Detection Method based on Space Box

Routes Conflict detection is of great importance to air traffic control. Due to previous route conflict detection algorithm has a large number of computations, high time complexity and low reliability. This paper proposes a fast route conflict detection method based on the space box. Firstly, the space to be detected is divided into several space boxes, and the space box numbers of the track in the airspace are calculated according to the track coordinates. Secondly, in the case that multiple batches of tracks exist in the same space box, the location of the tracks is predicted according to the current track parameters. If the distance between the tracks gradually decreases, it is judged that the tracks have conflict risk and an alarm is required. The simulation results show that the algorithm has low time complexity, and high efficiency, and is suitable for parallel processing and large-scale route conflict detection, which has high application value.

Aircraft Autonomous Separation Assurance Based on Cooperative Game Theory

Transferring part of the separation assurance responsibilities from air traffic controllers to pilots during en route phases of flight can reduce the controllers’ workload while ensuring operational safety and improving operational efficiency in the airspace. For this new generation of distributed air traffic management mode, firstly use the conflict detection algorithm to determine whether a potential conflict exists between two aircraft, introduce cooperative game theory to autonomous separation assurance model for horizontal cross-conflict in a static wind field by forming a coalition of all aircraft involved in the potential conflict. The convex combination of minimum yaw angle and maneuver flight time is used as the strategic gain of the aircraft, and the welfare function of the coalition is maximized by changing the behavioral strategy of the aircraft. Finally, a horizontal cross-conflict scenario is set up for simulation experiments and compared with a centralized separation assurance strategy. The simulation results show the effectiveness of cooperative game theory, which is applied in distributed autonomous separation assurance.

An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts

Advanced network services, such as firewalls, policy-based routing, and virtual private networks, must rely on routers to classify packets into different flows based on packet headers and predefined filter tables. When multiple filters are overlapped, conflicts may occur, leading to ambiguity in the packet classification. Conflict detection ensures the correctness of packet classification and has received considerable attention in recent years. However, most conflict-detection algorithms are implemented on a conventional central processing unit (CPU). Compared with a CPU, a graphics processing unit (GPU) exhibits higher computing power with parallel computing, hence accelerates the execution speed of conflict detection. In this study, we employed a GPU to develop two efficient algorithms for parallel conflict detection: the general parallel conflict-detection algorithm (the GPCDA) and the enhanced parallel conflict-detection algorithm (the EPCDA). In the GPCDA, we demonstrate how to perform conflict detection through parallel execution on GPU cores. While in the EPCDA, we analyze the critical procedure in conflict detection as to reduce the number of matches required for each filter. In addition, the EPCDA adopts a workload balance method to enable load balancing of GPU execution threads, thereby significantly improving performance. The simulation results show that with the 100 K filter database, the GPCDA and the EPCDA execute conflict detection 2.8 to 13.9 and 9.4 to 33.7 times faster, respectively, than the CPU-based algorithm.

Collision Avoidance Strategy for Multivehicle Conflict on Common Rail

With the rapid expansion and application of bright logistics technology in the field of metallurgical production, in recent years, a type of intermediate granary storage and transportation system, signified by multiple child-parent rail car system, has gradually looked in steel plants, which effectively solves the drawbacks of crane craning in the intermediate warehouse area and improves the transportation competence of shop-level logistics. However, there are not many studies on conflict avoidance strategies for such multivehicle cooperative common rail transportation system in order to improve the ability of conflict detection and active avoidance in operation of such multivehicle transportation system. This paper draws on and improves the banker antideadlock conflict detection algorithm widely used in computer process management to detect conflicts in multivehicle on common rail system and effectively evades collision conflicts detected in the system by combining dynamic priority transfer with multiobjective time window offset. Finally, the validity of the improved banker algorithm conflict detection and priority transfer time window adjustment collision avoidance strategy is confirmed through an example analysis and comparison, providing a security guarantee for the efficient and intelligent operation of such common rail multivehicle logistics system, particularly for the exploration of the logistics common rail multivehicle collision avoidance strategy in metallurgical heavy-duty workshop.

Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles’ security issues pose a major challenge to the air–ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

Distributed Motion Coordination for Multirobot Systems Under LTL Specifications

This article investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace, each of which is assigned a linear temporal logic specification. Based on the realistic assumptions that each robot is subject to both state and input constraints and can have only local view and local information, a fully distributed multirobot motion coordination strategy is proposed. For each robot, the motion coordination strategy consists of three layers. An offline layer precomputes the braking area for each region in the workspace, the controlled transition system, and a so-called potential function. An initialization layer outputs an initially safely satisfying trajectory. An online coordination layer resolves conflicts when one occurs. The online coordination layer is further decomposed into three steps. First, a conflict detection algorithm is implemented, which detects conflicts with neighboring robots. Whenever conflicts are detected, a rule is designed to assign dynamically a planning order to each pair of neighboring robots. Finally, a sampling-based algorithm is designed to generate local collision-free trajectories for the robot, which at the same time guarantees the feasibility of the specification. Safety is proven to be guaranteed for all robots at any time. The effectiveness and the computational tractability of the resulting solution is verified numerically by two case studies.

A Research for Executable Path Automatic Generation Method Based on EFSM

Extended finite state machine (EFSM) is currently one of the most widely used model in the field of software testing. EFSM model is an enhanced model based on finite state machine (FSM). Automated test data generation is still a challenging problem due to the complexity of EFSM which extends the input and output parameters, context variables as well as the predicate condition. These reasons lead to conflict of the context variable with the enable conditions in the transition path. In order to avoid infeasible path generation, this paper proposes a method based on modified breadth first search to generate feasible transition path (MBFS-FTP). To solve the problem of state explosion in path generation, this paper converts state diagram to transition diagram on EFSM model. In order to make the EFSM static model can be driven execution, this paper uses UML model and generate executable model, so that implements EFSM execute dynamically. When using breadth-first search algorithm (MBFS) on every target transition to generate an executable transition path, the conflict detection algorithm is utilized to the transition path for conflict decision, avoiding the occurrence of an infeasible transition path. Considering target transition has multiple feasible transition paths, this paper combines suggested penalized value of definition-predicate-use (def-p-use) pair and length of feasible transition path, and then develops the measurement method of feasible transition path, and obtained a set of feasible transition path containing all of transitions. Through the experiment on two actual EFSM model, verifying the effectiveness of MBFS-FTP for feasible transition automatic generation, the experimental results show that MBFS-FTP can reduce the feasible transition path length, and make transition paths are more easily to be triggered at the times of generating test cases lately, furthermore, it can improve the efficiency of generate feasible transition path and save a lot of time.