232
Sensemaking in Safety Critical and Complex Situations
The third phase of this study successfully applied sensemaking data/ frame model
and ANSI/ HFES-100 standard to a HFACS model-based AIB report. It highlighted
at least one additional HF/ E issue in the GCS, which was not mentioned as an influencing factor in the mishap AIB report. The phase III results found that the dual use
of the condition lever that was inadvertently left in an incorrect mode resulted in
shutting off fuel supply and causing the mishap. It was found to be counter intuitive
that the same condition lever was used to control a camera iris in one mode and a
fuel shutoff switch in another. The HFACS model-based report had listed lost receive
signal from the UAS and an improper use of checklist as the reason for the mishap.
The position of the control lever was not listed as the main cause of the mishap.
Moreover, the application of ANSI/ H FES-100 standard resulted in specification for
dual mode controls. The standard states when keys have collateral function, their
mode of operation shall be clearly indicated, which was obviously not the case in this
GCS, resulting in an overlooked HF/ E that was not captured by the HFACS model.
Moreover, this study helped demonstrate the applicability of sensemaking data/
frame model to extract information from the AIB reports that may have been overlooked previously. It also highlighted the fact that the current taxonomies/ models
used to investigate human factors in manned aircrafts may not be sufficient to investigate HF/ E in UAS GCS. Adding sensemaking and human factors experts to the
UAS mishap investigation process, or even in the interpretation of an AIB report, can
provide a profound understanding of the true nature cause of an accident. Once the
HF/ E deficiency is found it can then be fixed retroactively.
Developing a resilient system is a lifelong iterative process where repeated application of sensemaking process will result in an HF/ E-resilient system design. The
real purpose of sensemaking is to provide meaningful feedback to the system when
it is time to alter its understanding of problem situations. Therefore, the adaptive and
resilient sensemaking process requires ways of identifying irregularities and conditions that require a border change in system design ( Hoffman & Hancock, 2017).
REFERENCES
2018 RPAS Yearbook. ( 2018, October). RPAS: The Global Perspective. Paris, France:
Blyenburgh & Co.
Agarwal, S., Murphy, R., & Adams, J. ( 2014). Characteristics of indoor disaster environments
for small UASs. 2014 IEEE International Symposium on Safety, Security, and Rescue
Robotics (2014) (pp. 1–6).
Andersen, H., Bove, T., Isaac, A., Kennedy, R., Kirwan, B., & Shorrock, T. ( 2002). Short
Report on Human Performance Models and Taxonomies of Human Error in ATM
(HERA). Brussels: EUROCONTROL.
ANSI/HFES-100. (2007). Human Factors Engineering of Computer Workstations. Santa
Monica, CA: Human Factors and Ergonomics Society.
Baur, J. ( 2007). The NASA Dryden Flight Research Center Unmanned Aircraft System Service
Capabilities. Washington, DC: NASA.
Berni, J., Zarco-Tejada, P., Suarez, L., & Fereres, E. ( 2009). Thermal and Narrowband
Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial
Vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.
Brittain, J. ( 2009). Electrical Engineering Hall of Fame: Charles F. Kettering. Proceedings of
the IEEE, 97(10), 1737–1739.
Sensemaking in Safety Critical and Complex Situations
The third phase of this study successfully applied sensemaking data/ frame model
and ANSI/ HFES-100 standard to a HFACS model-based AIB report. It highlighted
at least one additional HF/ E issue in the GCS, which was not mentioned as an influencing factor in the mishap AIB report. The phase III results found that the dual use
of the condition lever that was inadvertently left in an incorrect mode resulted in
shutting off fuel supply and causing the mishap. It was found to be counter intuitive
that the same condition lever was used to control a camera iris in one mode and a
fuel shutoff switch in another. The HFACS model-based report had listed lost receive
signal from the UAS and an improper use of checklist as the reason for the mishap.
The position of the control lever was not listed as the main cause of the mishap.
Moreover, the application of ANSI/ H FES-100 standard resulted in specification for
dual mode controls. The standard states when keys have collateral function, their
mode of operation shall be clearly indicated, which was obviously not the case in this
GCS, resulting in an overlooked HF/ E that was not captured by the HFACS model.
Moreover, this study helped demonstrate the applicability of sensemaking data/
frame model to extract information from the AIB reports that may have been overlooked previously. It also highlighted the fact that the current taxonomies/ models
used to investigate human factors in manned aircrafts may not be sufficient to investigate HF/ E in UAS GCS. Adding sensemaking and human factors experts to the
UAS mishap investigation process, or even in the interpretation of an AIB report, can
provide a profound understanding of the true nature cause of an accident. Once the
HF/ E deficiency is found it can then be fixed retroactively.
Developing a resilient system is a lifelong iterative process where repeated application of sensemaking process will result in an HF/ E-resilient system design. The
real purpose of sensemaking is to provide meaningful feedback to the system when
it is time to alter its understanding of problem situations. Therefore, the adaptive and
resilient sensemaking process requires ways of identifying irregularities and conditions that require a border change in system design ( Hoffman & Hancock, 2017).
REFERENCES
2018 RPAS Yearbook. ( 2018, October). RPAS: The Global Perspective. Paris, France:
Blyenburgh & Co.
Agarwal, S., Murphy, R., & Adams, J. ( 2014). Characteristics of indoor disaster environments
for small UASs. 2014 IEEE International Symposium on Safety, Security, and Rescue
Robotics (2014) (pp. 1–6).
Andersen, H., Bove, T., Isaac, A., Kennedy, R., Kirwan, B., & Shorrock, T. ( 2002). Short
Report on Human Performance Models and Taxonomies of Human Error in ATM
(HERA). Brussels: EUROCONTROL.
ANSI/HFES-100. (2007). Human Factors Engineering of Computer Workstations. Santa
Monica, CA: Human Factors and Ergonomics Society.
Baur, J. ( 2007). The NASA Dryden Flight Research Center Unmanned Aircraft System Service
Capabilities. Washington, DC: NASA.
Berni, J., Zarco-Tejada, P., Suarez, L., & Fereres, E. ( 2009). Thermal and Narrowband
Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial
Vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.
Brittain, J. ( 2009). Electrical Engineering Hall of Fame: Charles F. Kettering. Proceedings of
the IEEE, 97(10), 1737–1739.