Jason Gauci

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Bio Data

Dr Jason Gauci graduated in electrical engineering from the University of Malta in 2005. In 2010 he was awarded a PhD in Aerospace Engineering from Cranfield University (UK) for his research on obstacle detection around aircraft through the use of computer vision. He then worked as a software and systems engineer within the UK aerospace and automotive industries, before returning to Malta in 2014 to join the Institute of Aerospace Technologies where he is currently employed as a resident academic and lecturer. His research interests include computer vision, safety-critical system design, avionics display design, and UAS.


The RAID project is one of a number of projects promoted and co-financed by SESAR Joint Undertaking in the large spectrum of activities addressing the seamless integration of the RPAS in unrestricted airspace. In the RAID vision, the SJU global aim was that of evaluating, from a primary involved stakeholders point-of-view, what kind of peculiarities could arise in integrating RPAS with normal commercial manned air traffic. A second key element was the short-term vision it considers for the required evaluation. The project addressed some relevant KPAs of the SESAR Performance Framework, namely Human Performance, Safety and Security, further defined by a number of specific objectives which were measured in Real-Time Simulations (RTS) and in Flight Trials. Since the integration of RPAS in unsegregated airspace involves safety, security and human performance aspects related to both the adoption of new specific procedures and the introduction of new technologies (e.g. Detect And Avoid), RAID objectives specifically addressed the evaluation of procedures and technologies in:

  • Using Temporary segregated areas, as it can be peculiarly done in RPAS missions;
  • Traffic separation managed by air traffic controllers dealing with manned and unmanned intruders;
  • Emergency conditions management during jamming/spoofing of the command and control Link;
  • Cooperative traffic separation between remote pilot and controllers using an airborne automatic dependent surveillance – broadcast (ADS-B) based Detect And Avoid (DAA) decision support system.
  • Fully automated and autopilot modes.

The most relevant conclusions, as emerged from both the Real-Time Simulations and assessed in Flight Trials, can be summarized as follows:

  • The management of an RPAS in executing its typical mission can be safely accomplished by both remote pilots and Air Traffic Controllers;
  • The management of emergency conditions, notably those arising from C2L loss of signals, can also be accomplished by remote Pilot and ATC, when the expected behavior of the aircraft in those specific conditions is well shared among all involved stakeholders;
  • The introduction of new technologies, namely the DAA technology, is well accepted by remote pilot and ATCOs, but again RPAS and its systems expected behavior has to be well shared between pilots and ATCOs,
  • Specific training sessions can efficiently support the need of specific knowledge of RPAS performance and behavior, in nominal and non-nominal conditions.