The first objective of this chair is to develop new AI decision support tools for assisting air traffic management operators, mainly air traffic controllers and pilots, in order to enhance their efficiency and to increase the overall capacity of the air traffic system.
The chair objective is to go a step further by proposing now AI approaches to target full
automation in the framework of large scale urban mobility in a wide sense. For such system,
aerial vehicles (UAV) and ground vehicles (autonomous cars) have to self organize in order to ensure safe
and efficient operations.
For such an automatic system, there is no centralized entity which organizes the traffic as in traditional ATM.
We propose to develop new AI algorithms for organizing traffic in such a full automatic framework for large scale air trafic management.
The main applications of the team's research are:
- Airspace design (e.g. roads, control sectors)
- Optimization of air traffic (continental and oceanic strategic, pre-tactical and tactical)
- 4D optimization of aircraft trajectories
- Optimization of airport traffic (eg landing sequencing, taxiing, allocation of runways and doors)
- Drones (ex: planning of drone trajectories, planning of missions)
Chair holder : Pr Daniel Delahaye ENAC
Co- chairs :
- Nicolas Couellan (ENAC)
- Emmanuel Rachelson (ISAE)
Senior collaborating researcher
- Stéphane Puechmorel (ENAC)
- Sylvain Roudière,
- Dinh-Thinh Hoang,
- Yael Zorah
- Gabriel Jarry,
- Philippe Monmousseau
AI, the new transport eldorado
Civil planes, drones, land links: optimization in transport is an issue of safety and fluidity, but also a major environmental issue. With AI, a new generation of transport is taking shape. Demonstration with Daniel Delahaye, teacher-researcher at the National School of Civil Aviation (ENAC).