xii
Introduction
through data gathering and risk-based regulation. Unanticipated deviations are key
challenges in automated systems, together with how to design for human–automation
interaction and meaningful user involvement.
13 – A PPLICATION OF SENSEMAKING: DATA/FRAME MODEL,
TO UAS AIB REPORTS CAN INCREASE UAS GCS RESILIENCE
TO HUMAN FACTOR AND ERGONOMICS SHORTFALLS
Unmanned Aircraft Systems (UAS) have grown exponentially. The operators
remotely control the entire UAS flight from the Ground Control Station (GCS)
while sitting comfortably hundreds or even thousands of miles away. Its pilot safety
feature drove UAS’ initial development towards security, law enforcement and military. However, it also led to the elimination of standardized testing required for the
manned aircrafts. The hastily developed and deployed UAS lead to an increased
number of UAS mishaps. As studies show 69% of all mishaps are due to human
factors proliferation in GCS, and nearly 25% of those mishaps are directly related
to human factors and ergonomic (HF/E) shortfalls in GCS design. Nowadays, UAS
are being employed in several sectors. Nonetheless, UAS-specific standards and
methodical testing are still lacking. This study verifies the applicability of existing
ANSI/HFES-100 for computer workstation to UAS GCS, followed by a case study
to apply sensemaking (data/frame model) to UAS Accident Investigation Report
(AIB) to identify HF/E shortfalls in GCS that may have been overlooked previously. Once the HF/E shortfalls are identified, a human factor standard ANSI/
HFES-100 is used to resolve those issues in GCS retroactively, thus improving
UAS GCS resilience.
14 – CONSTRAINED AUTONOMY FOR A BETTER
HUMAN–AUTOMATION INTERFACE
Most industrial autonomous systems use an operator to handle situations beyond
the automation system’s capabilities. This means regular changes between automatic control and human control. A safe change from automatic to human control
requires, among other factors, that humans have enough time from being alerted to
the problem, until getting sufficient situational awareness to act safely, i.e. a maximum response time. This chapter explores this problem from the point of view of
the automation system and will provide a framework for describing and analysing it.
This is based on the definition of an operational envelope, which defines what the
control system, including humans and automation, needs to be able to handle in the
different system states, to achieve the system objectives. By observing constraints
on how automatic functions are implemented, it is possible to divide the operational
envelope into distinct areas based on the definition of a response deadline. This is the
minimum time a human has available to respond to any new situation that requires
human intervention. When response deadline is longer than the maximum response
time, it is safe to leave control to the automation and rely on alerts to muster the
human.
Introduction
through data gathering and risk-based regulation. Unanticipated deviations are key
challenges in automated systems, together with how to design for human–automation
interaction and meaningful user involvement.
13 – A PPLICATION OF SENSEMAKING: DATA/FRAME MODEL,
TO UAS AIB REPORTS CAN INCREASE UAS GCS RESILIENCE
TO HUMAN FACTOR AND ERGONOMICS SHORTFALLS
Unmanned Aircraft Systems (UAS) have grown exponentially. The operators
remotely control the entire UAS flight from the Ground Control Station (GCS)
while sitting comfortably hundreds or even thousands of miles away. Its pilot safety
feature drove UAS’ initial development towards security, law enforcement and military. However, it also led to the elimination of standardized testing required for the
manned aircrafts. The hastily developed and deployed UAS lead to an increased
number of UAS mishaps. As studies show 69% of all mishaps are due to human
factors proliferation in GCS, and nearly 25% of those mishaps are directly related
to human factors and ergonomic (HF/E) shortfalls in GCS design. Nowadays, UAS
are being employed in several sectors. Nonetheless, UAS-specific standards and
methodical testing are still lacking. This study verifies the applicability of existing
ANSI/HFES-100 for computer workstation to UAS GCS, followed by a case study
to apply sensemaking (data/frame model) to UAS Accident Investigation Report
(AIB) to identify HF/E shortfalls in GCS that may have been overlooked previously. Once the HF/E shortfalls are identified, a human factor standard ANSI/
HFES-100 is used to resolve those issues in GCS retroactively, thus improving
UAS GCS resilience.
14 – CONSTRAINED AUTONOMY FOR A BETTER
HUMAN–AUTOMATION INTERFACE
Most industrial autonomous systems use an operator to handle situations beyond
the automation system’s capabilities. This means regular changes between automatic control and human control. A safe change from automatic to human control
requires, among other factors, that humans have enough time from being alerted to
the problem, until getting sufficient situational awareness to act safely, i.e. a maximum response time. This chapter explores this problem from the point of view of
the automation system and will provide a framework for describing and analysing it.
This is based on the definition of an operational envelope, which defines what the
control system, including humans and automation, needs to be able to handle in the
different system states, to achieve the system objectives. By observing constraints
on how automatic functions are implemented, it is possible to divide the operational
envelope into distinct areas based on the definition of a response deadline. This is the
minimum time a human has available to respond to any new situation that requires
human intervention. When response deadline is longer than the maximum response
time, it is safe to leave control to the automation and rely on alerts to muster the
human.