(Georgia Institute of Technology, 2021-08)
Speirs, Andrew H.; Ramee, Coline; Payan, Alexia P.; Mavris, Dimitri N.; Feigh, Karen M.
Helicopter pilots face unique challenges with regard to adverse weather when compared to fixed-wing pilots. Rotorcraft typically operate at lower altitudes in off-field areas that are not always well covered by weather reporting stations. Although recent technological advances have increased the amount of weather data that pilots can access in the cockpit, weather remains a factor in 28% of fatal helicopter accidents. In this work, commercial helicopter pilots were surveyed and interviewed to better understand how they gather and process weather information, what the perceived limitations of current weather tools are, and how their decision-making process is affected by the information they gather and/or receive. Pilots were found to use a wide variety of weather sources for their initial go or no-go decision during the preflight phase, but use fewer weather sources in the cockpit while in-flight. Pilots highlighted the sparsity and sometimes inaccuracy of the weather information available to them in their prototypical operational domain. To compensate, they are forced to rely on local and experiential weather knowledge to supplement weather reports while still working to mitigate other external pressures. Based on the literature and on results from this work, recommendations are made to address the weather-related gaps faced by the rotorcraft community. This includes the installation of additional weather reporting stations outside of airports and densely populated areas, the further promotion of the HEMS tool to helicopter pilots in all industries, the development of weather tools capable of visualizing light precipitation such as fog, and the development of in-flight graphical displays that can help reduce the cognitive workload of interpreting weather information.
(Georgia Institute of Technology, 2017-11)
Jaunzemis, Andris D.; Minotra, Dev; Holzinger, Marcus J.; Feigh, Karen M.; Chan, Moses W.; Shenoy, Prakash P.
Realistic decision-making often occurs with insufficient time to gather all possible
evidence before a decision must be rendered, requiring an efficient process for prioritizing
between potential action sequences. This work aims to develop a decision support
system for tasking sensor networks to gather evidence to resolve hypotheses in
the face of ambiguous, incomplete, and uncertain evidence. Studies have shown that
decision-makers demonstrate several biases in decisions involving probability judgement,
so decision-makers must be confident that the evidence-based hypothesis resolution
is strong and impartial before declaring an anomaly or reacting to a conjunction
analysis. Providing decision-makers with the ability to estimate uncertainty and ambiguity
in knowledge has been shown to augment effectiveness. The proposed framework,
judicial evidential reasoning (JER), frames decision-maker questions as rigorously
testable hypotheses and employs an alternating-agent minimax optimization on
belief in the null proposition. This approach values impartiality in addition to time efficiency:
an ideal action sequence gathers evidence to quickly resolve hypotheses
while guarding against bias. JER applies the Dempster-Shafer theory of belief functions
to model knowledge about hypotheses and quantify ambiguity, and adversarial optimization techniques are used to make many-hypothesis resolution computationally
tractable. This work includes derivation and application of the JER formulation to
a GTO insertion maneuver anomaly scenario.