{"id":133,"date":"2020-05-07T19:21:17","date_gmt":"2020-05-07T19:21:17","guid":{"rendered":"http:\/\/mahalo01.lr.tudelft.nl\/?page_id=133"},"modified":"2024-05-29T13:31:52","modified_gmt":"2024-05-29T13:31:52","slug":"outcomes","status":"publish","type":"page","link":"https:\/\/mahalo01.lr.tudelft.nl\/?page_id=133","title":{"rendered":"Materials"},"content":{"rendered":"

Project Deliverables<\/h1>\n

Deliverable D2.1: State of the Art Review (SOAR)<\/a><\/p>\n

Deliverable D2.2: ConOps<\/a><\/p>\n

Deliverable D3.1: Machine Learning Report<\/a><\/p>\n

Deliverable D3.2: Machine Learning Demonstrator<\/a><\/p>\n

Deliverable D4.1: E-UI Design Document<\/a><\/p>\n

Deliverable D4.1: Video Demonstrator<\/a><\/p>\n

Deliverable D4.2: E-UI Validation Report<\/a><\/p>\n

Deliverable D5.1: Integration report<\/a><\/p>\n

Deliverable D5.2: Simulation 1 report<\/a><\/p>\n

Deliverable D6.1: Experimental Design report<\/a><\/p>\n

Deliverable D6.2: Field Simulation report<\/a><\/p>\n

Deliverable D7.2: 1st Workshop results<\/a><\/p>\n

Deliverable D7.3: 2nd Workshop results<\/a><\/p>\n

Deliverable D7.4: Final Project Results<\/a><\/p>\n

Supplemental Materials to D6.2<\/h2>\n

\"\"SectorX<\/strong>, a Java-based, medium-fidelity ATC research simulator developed by TU Delft, was used in<\/p>\n

MAHALO to collect data for the real-time human-in-the-loop simulations. The simulator is configurable to mimick any existing Plan View Display, allows a controller to interact with aircraft via either a clearance menu or touch input device. It can be customised to display various decision-support tools for conflict detection and resolution, ranging from classic tools such as VERA (used by MUAC) and the Separation Monitor, towards novel ecological interfaces developed by TU Delft (e.g., Solution Space Diagram and the time- and distance-based travel space). The simulator is also capable of using (and visualising) high-resolution wind data (GRIB files), modeling pilot delays and simulating basic conflict detection & resolution algorithms (at various levels of automation), but not all of these aspects were not used in MAHALO for the sake of experimental control.<\/p>\n

For research purposes, SectorX allows the creation of custom scenarios (draw airspaces, add flights and routes, define conflicts, etc.) or import existing airspaces, simulate a set of scenarios according to a customisable playlist and replay recorded sessions. The recorded radar states and events (e.g., human and\/or automation actions) are written in XML files that can easily be parsed with Python for further processing (e.g., to calculate statistics).<\/p>\n

The animations below are snippets from the MAHALO trials.<\/p>\n


\n\"\" <\/a>
SectorX: Conformance pre-test session<\/figcaption><\/figure>\n

\n\"\" <\/a>
SectorX: Final experiment session – nudging an advisory<\/figcaption><\/figure>\n

\n\"\" <\/a>
SectorX: Final experiment session – rejecting an advisory and interacting with the other conflict aircraft<\/figcaption><\/figure>\n

Articles, Posters, Presentations<\/h1>\n

SESAR AI White Paper<\/a><\/p>\n

SID2022: Personalized and transparent AI support for ATC conflict detection and resolution: an empirical study<\/a><\/p>\n

SID2022: Paper presentation<\/a><\/p>\n

RPAS and AI final dissemination event: MAHALO presentation slides<\/a><\/p>\n

SID2021: Human-interpretable input for Machine Learning<\/a>
\nin Tactical Air Traffic Control<\/p>\n

SID2020: MAHALO poster (PDF)<\/a><\/p>\n

SID2020: MAHALO poster description (PDF)<\/a><\/p>\n

DASC2020: Building Transparent and Personalized AI <\/a>Support <\/u>in Air Traffic Control (PDF)<\/a><\/p>\n

Videos<\/h1>\n

SESAR AI White Paper video<\/strong><\/p>\n