公开(公告)号：US20230230490A1

公开(公告)日：2023-07-20

申请号：US18008436

申请日：2021-06-01

Applicant: THALES

Inventor： Paul LALISSE-BAUVIN ,  Béatrice PESQUET-POPESCU ,  Andrei PURICA ,  David LAVILLE

IPC: G08G5/06 ,  G08G5/00

CPC classification number: G08G5/065 ,  G08G5/0043

Abstract: A computer-implemented method is provided for training a supervised machine learning engine able to predict characteristics of aircraft trajectories from parameters of an aircraft, and environment parameters of the aircraft trajectory. A system able to train the supervised machine learning engine, a system for using the engine, and a computer-implemented method for using the engine are provided. The methods and systems provided are particularly useful for air traffic flow management applications.

公开(公告)号：US20240304096A1

公开(公告)日：2024-09-12

申请号：US18286545

申请日：2022-04-14

Applicant: THALES

Inventor： Laurent LALUQUE ,  Chris DESEURE ,  Jacques EDELINE ,  Laurent FLOTTE ,  David LAVILLE

IPC: G08G5/00

CPC classification number: G08G5/0039 ,  G08G5/0043 ,  G08G5/006 ,  G08G5/0091

Abstract: A method for communication between an air traffic control system and a communication module, the communication method includes a step of determining environmental optimization time slots based on the air traffic absorption capacities of the various flight information regions, the environmental optimization time slots at least partially covering one or more flight information regions, each environmental optimization time slot having an associated efficiency level; a step of negotiation, between the pilot via the communication module and the air traffic control system, to negotiate changes to the flight plan on the basis of the environmental optimization time slots and the environmental optimization levels of the environmental optimization time slots.

公开(公告)号：US20220270497A1

公开(公告)日：2022-08-25

申请号：US17631486

申请日：2020-07-13

Applicant: THALES

Inventor： Béatrice PESQUET-POPESCU ,  Dominique LATGE ,  David LAVILLE ,  Fateh KAAKAI

IPC: G08G5/00 ,  G06N20/00

Abstract: A computation of the processing complexity of an air-traffic-control situation is provided. In particular, the processing complexity of an air-traffic-control situation is computed using a supervised learning engine, trained using the result of analytical functions for computing the processing complexity of air-traffic-control situations.