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EN
Forecasting future trajectories of intelligent agents presents a formidable challenge, necessitating the analysis of intricate scenarios and uncertainties arising from agent interactions. Consequently, it is judicious to contemplate the establishment of inter-agent relationships and the assimilation of contextual semantic information. In this manuscript, we introduce HTTNet, a comprehensive framework that spans three dimensions of information modeling: (1) the temporal dimension, where HTTNet employs a time encoder to articulate time sequences, comprehending the influences of past and future trajectories; (2) the social dimension, where the trajectory encoder facilitates the input of trajectories from multiple agents, thereby streamlining the modeling of interaction information among intelligent agents; (3) the contextual dimension, where the TF-map encoder integrates semantic scene input, amplifying HTTNet cognitive grasp of scene information. Furthermore, HTTNet integrates a hybrid modeling paradigm featuring CNN and transformer, transmuting map scenes into feature information for the transformer. Qualitative and quantitative analyses on the nuScenes and interaction datasets highlight the exceptional performance of HTTNet, achieving 1.03 minADE10 and a 0.31 miss rate on nuScenes, underscoring its effectiveness in multi-agent trajectory prediction in complex scenarios.
EN
In this paper, the route prediction for a person in water was performed on the basis of a developed graph algorithm. This person drifted in water under the influence of surface currents and wind. The total drift route for the person in water was established as the route in a weighted directed graph. Vertices of this graph correspond to given points within a given basin. Additionally, the graph’s edges show possible directions of the overall human drift. The weight of the given edge describes the difference between the gradient of the edge and the total drift direction calculated on the basis of surface current field data and wind field data. An application has been created on the basis of a given algorithm which might be used to support the search for survivors in coastal areas (e.g. port basins, basins adjacent to the port, bays and sea areas) for which hydrodynamic models reliably reflect local phenomena.
EN
This paper proposes a model of the interactions between surface currents and small, moving objects. These objects are immersed in water so that the part extending above the water is no larger than a human head. These interactions are defined as the weighted-directed graph. The basis for determining the edge weights are the directions of the surface currents. The speeds of these currents are used to calculate the time of moving objects. According to the modelling method of the surface-current influence on small objects, presented in this paper, it is possible to implement an application supporting search-and-rescue-operation planning. This method can be used to locate small objects, such as survivors, when planning search-and-rescue operations. Thus, the routes of these objects moving together with surface-water masses can be predicted using this method.
PL
Artykuł omawia trendy we współczesnych systemach ATM na przykładzie rozwiązań wdrażanych w systemie PEGASUS_21. System PEGASUS_21 wykorzystuje mechanizm przewidywania trajektorii, który pozwala wykorzystać go w szerszym zakresie niż tylko dozorowanie radarowe. Nowe funkcje obejmują m.in.: wyznaczanie sekwencji sektorów i selektywną prezentację list sektorowych, elektroniczną koordynację, średnioterminowe przewidywanie konfliktów, przewidywanie kolizyjności z dynamicznie aktywowanymi elementami przestrzeni, ocenę obciążenia sektorów. Równolegle wdrażane są również nowe funkcje niezależne od trajektorii, takie jak wdrożenie scentralizowanego systemu przydziału kodów transponderów CCAMS, oraz projekt uaktualnienia planu lotu FPL 2012.
EN
This paper describes new trends in ATM systems, illustrated by solutions developed in PEGASUS_21. PEGASUS_21 heavily relies on trajectory prediction algorithms, allowing to extend its features far beyond simple surveillance. New features include: sector sequence designation and selective displaying of sector lists, silent coordination, medium term collision detection, predicting collisions with dynamic airspace structures, dynamic assessment of future sectors load. At the same time other features are being implemented, not depending on trajectory prediction – such as implementation of centralized codes assignment project – CCAMS, or updating of flight plan form contents - FPL 2012.
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