TY - GEN
T1 - Human-Machine Interactions in Very-Low-Level UAS Operations and Traffic Management
AU - Pongsakornsathien, Nichakorn
AU - Gardi, Alessandro
AU - Sabatini, Roberto
AU - Kistan, Trevor
AU - Ezer, Neta
N1 - Funding Information:
ACKNOWLEDGMENT The authors wish to thank and acknowledge THALES Australia, and Northrop Grumman Corporation for separately supporting different aspects of this work under the collaborative research projects RE-02544-0200315666 and RE-03163-0200317164 respectively.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - With the proliferation of Unmanned Aircraft Systems (UAS) in low-altitude airspace and a growing interest in new Urban Air Mobility (UAM) solutions, the Air Traffic Controller (ATCo)'s responsibility to ensure safety and efficiency of operations can no longer be fulfilled with the conventional air traffic control paradigm. Hence, a new increasingly autonomous Decision Support System (DSS) specifically designed for integrated manned/UAS Traffic Management (UTM) is of paramount importance. This DSS makes use of advanced traffic flow and airspace management concepts, but to ensure effective teaming between the human and the system in challenging situations, the nature of their roles and responsibilities is to be analysed in depth and reflected in the design of suitable Human-Machine Interfaces and Interactions (HMI2). The task analysis presented in this paper assessed the interdependencies between the human and the machine following the Observe-Orient-Decide-Act (OODA) framework. The paper focuses on the management of urban airspace, which is partitioned based on navigation performance. The human-machine workflow is presented and discussed, highlighting the proposed interactions in each subtask. To support closed-loop interactions and enhance system integrity, the UTM DSS makes use of the Cognitive HMI2 concept, which is also briefly outlined in this paper.
AB - With the proliferation of Unmanned Aircraft Systems (UAS) in low-altitude airspace and a growing interest in new Urban Air Mobility (UAM) solutions, the Air Traffic Controller (ATCo)'s responsibility to ensure safety and efficiency of operations can no longer be fulfilled with the conventional air traffic control paradigm. Hence, a new increasingly autonomous Decision Support System (DSS) specifically designed for integrated manned/UAS Traffic Management (UTM) is of paramount importance. This DSS makes use of advanced traffic flow and airspace management concepts, but to ensure effective teaming between the human and the system in challenging situations, the nature of their roles and responsibilities is to be analysed in depth and reflected in the design of suitable Human-Machine Interfaces and Interactions (HMI2). The task analysis presented in this paper assessed the interdependencies between the human and the machine following the Observe-Orient-Decide-Act (OODA) framework. The paper focuses on the management of urban airspace, which is partitioned based on navigation performance. The human-machine workflow is presented and discussed, highlighting the proposed interactions in each subtask. To support closed-loop interactions and enhance system integrity, the UTM DSS makes use of the Cognitive HMI2 concept, which is also briefly outlined in this paper.
KW - Decision Making
KW - Decision Support System
KW - Human-Machine Interface
KW - Human-Machine System
KW - UAS Traffic Management
KW - Urban Air Mobility
UR - http://www.scopus.com/inward/record.url?scp=85091478191&partnerID=8YFLogxK
U2 - 10.1109/DASC50938.2020.9256757
DO - 10.1109/DASC50938.2020.9256757
M3 - Conference contribution
AN - SCOPUS:85091478191
T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings
BT - DASC 2020 - 39th Digital Avionics Systems Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020
Y2 - 11 October 2020 through 16 October 2020
ER -