Analysis of pilot control strategy

Methods for nonintrusive identification of pilot control strategy and task execution dynamics are presented along with examples based on flight data. The specific analysis technique is Nonintrusive Parameter Identification Procedure (NIPIP), which is described in a companion user's guide (NASA CR-170398). Quantification of pilot control strategy and task execution dynamics is discussed in general terms followed by a more detailed description of how NIPIP can be applied. The examples are based on flight data obtained from the NASA F-8 digital fly by wire airplane. These examples involve various piloting tasks and control axes as well as a demonstration of how the dynamics of the aircraft itself are identified using NIPIP. Application of NIPIP to the AFTI/F-16 flight test program is discussed. Recommendations are made for flight test applications in general and refinement of NIPIP to include interactive computer graphics

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 367)

This bibliography lists 205 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System during Aug. 1992. Subject coverage includes the following: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Effect of Active Learning on Instrument Rated Pilots’ Knowledge and Self-Efficacy

The study examined the effect of active scenario-based training on the knowledge and self-efficacy of instrument rated pilots who were not instrument current. Additionally, this study addressed an issue that was not present in the existing literature by validating the potential of using at-home personal computer scenario-based simulation for instrument currency. The current method of maintaining instrument currency does not require any context or scenario-based training. At a minimum, a pilot must perform a specific number of instrument approaches and holds in an airplane, approved simulator, or training device. Sixty-two non-current instrument rated pilots who represented the U.S. general aviation pilot population participated in the study. The participants were divided into three experimental groups, where each group received a different training method with varying levels of active learning. The first group experienced passive learning (only reading), the second group experienced some active learning (flying simulated approaches), and the third group experienced the highest degree of active learning (flying approach scenarios requiring decisions). Before and after the training, each of the participants took knowledge tests and self-efficacy questionnaires as a measure of training effectiveness. The results show that the increase of knowledge scores between pre-training and post-training was significant regardless of training type, F(1,57) = 184.977, p \u3c .001, η2 = .764. Additionally, the results show that the increase in self efficacy scores between pre-training and post-training was significant, F(1, 57) = 299.409, p \u3c .001, η2 = .840. The increase in self-efficacy score was significant between the passive method of training, reading, and the most active method of training full scenarios with decision making involved, t(38) = -2.653, p = .012, r = .395. Using active learning through personal computer-based flight scenarios is an effective method of refresher training for instrument rated pilots who are not instrument current

Semi-Structured Decision Processes: A Conceptual Framework for Understanding Human-Automation Decision Systems

The purpose of this work is to improve understanding of existing and proposed decision systems, ideally to improve the design of future systems. A "decision system" is defined as a collection of information-processing components -- often involving humans and automation (e.g., computers) -- that interact towards a common set of objectives. Since a key issue in the design of decision systems is the division of work between humans and machines (a task known as "function allocation"), this report is primarily intended to help designers incorporate automation more appropriately within these systems. This report does not provide a design methodology, but introduces a way to qualitatively analyze potential designs early in the system design process. A novel analytical framework is presented, based on the concept of "semi-Structured" decision processes. It is believed that many decisions involve both well-defined "Structured" parts (e.g., formal procedures, traditional algorithms) and ill-defined "Unstructured" parts (e.g., intuition, judgement, neural networks) that interact in a known manner. While Structured processes are often desired because they fully prescribe how a future decision (during "operation") will be made, they are limited by what is explicitly understood prior to operation. A system designer who incorporates Unstructured processes into a decision system understands which parts are not understood sufficiently, and relinquishes control by deferring decision-making from design to operation. Among other things, this design choice tends to add flexibility and robustness. The value of the semi-Structured framework is that it forces people to consider system design concepts as operational decision processes in which both well-defined and ill-defined components are made explicit. This may provide more insight into decision systems, and improve understanding of the implications of design choices. The first part of this report defines the semi-Structured process and introduces a diagrammatic notation for decision process models. In the second part, the semi-Structured framework is used to understand and explain highly evolved decision system designs (these are assumed to be representative of "good" designs) whose components include feedback controllers, alerts, decision aids, and displays. Lastly, the semi-Structured framework is applied to a decision system design for a mobile robot.Charles Stark Draper Laboratory, Inc., under IR&D effort 101

How Well Do Service Concepts Apply to Digital Services and Service Digitalization?

This paper explores the extent to which typical service concepts apply to digital service (DS) and service digitalization. It defines service, service systems, digital, digitalization, digital objects, digital agents, digital service, and service digitalization. Application of those definitions to four real world cases explores how well concepts from the service literature describe DS and service digitalization

Multimodal Perception and Multicriterion Control of Nested Systems

This report reviews the operational demands made of a Shuttle pilot or commander within the context of a proven empirical methodology for describing human sensorimotor performance and whole-body coordination in mechanically and perceptually complex environments. The conclusions of this review pertain to a) methods for improving our understanding of the psychophysics and biomechanics of visual/manual control and whole-body coordination in space vehicle cockpits; b) the application of scientific knowledge about human perception and performance in dynamic inertial conditions to the development of technology, procedures, and training for personnel in space vehicle cockpits; c) recommendations for mitigation of safety and reliability concerns about human performance in space vehicle cockpits; and d) in-flight evaluation of flight crew performance during nominal and off-nominal launch and reentry scenarios

Effects of Online Training on Aircrew Monitoring Behaviors: A Field Study

Data from aircraft accidents and line observation studies indicate that inadequate pilot monitoring is a growing safety concern. In the cockpit environment, pilots who fail to properly manage their workload commit more monitoring errors. Given the lack of training and educational programs available to pilots which emphasize improving their monitoring skills, more research is needed to assess the usefulness of types of training that can be used to improve pilots\u27 monitoring. This research project sets out to determine if the potential exists to enhance pilots\u27 monitoring skills through online training. For this study, 40 military helicopter pilots (participants) were divided into two training groups: (1) an online training group, which completed a 20-minute web-based training module, and (2) a control group, which read a 20-minute article on aviation safety. Within each group, the pilots were paired and completed two training events in a flight simulator as part of their normal duties. The effects of the training were evaluated using Kirkpatrick\u27s multi-level framework: reactions, learning, behaviors, and results. First, all pilots receiving training were surveyed to capture the trainees\u27 perceptions of satisfaction and utility of the training. Second, all pilots were given a multiple-choice test to assess the effect the training had on learners\u27 knowledge of the training objectives. Next, the researchers observed, via video recording, both groups\u27 behaviors during flight simulator events. The researchers recorded occurrences of four behavior markers as the crews flew multiple instrument approaches. The researchers used two of these markers to study transfer of training and two markers to examine positive vs. negative outcomes at critical tasks during the simulated flights. The results show positive changes in the reactions, learning, and behavior dimensions, lending support to the effectiveness of relatively inexpensive online training to teach monitoring skills

The Effect of Transactive Memory and Collective Efficacy on Aircrew Performance

The use of teams is becoming prevalent in American organizations. The United States Air Force for example, employs aircrew teams on the majority of their aircraft. This thesis focuses on system and motivational variables that influence the performance of aircraft teams. Two potentially important team variables are identified and examined in three research studies. Transactive memory is a system which combines the knowledge possessed by individual team members with a shared awareness of who knows what, who is good at what, and who does what. Collective efficacy is the group\u27s collective belief that it can perform a specific task. This research tests these two constructs as competing constructs in explaining team performance. A laboratory and two field studies are conducted to determine the effects of transactive memory and collective efficacy on team performance. The results indicate that transactive memory has a consistent and positive relationship with performance across studies. However, the relationship failed to reach statistical significance due to small sample sizes. Change in the composition of the team due to turnover is shown to be detrimental to transactive memory. In addition, transactive memory makes important contributions to the team\u27s collective efficacy. In operational environments. collective efficacy is significantly related to higher performance. A confident team is a more effective team. These results are discussed in terms of their theoretical and practical significance

Special Investigation Report: Commercial Space Launch Incident, Launch Procedure Anomaly Orbital Sciences Corporation PEGASUS/SCD-1, 80 Nautical Miles East of Cape Canaveral, Florida, February 9, 1993

This report explains the procedural anomaly that occurred during the launch sequence of an Orbital Sciences Corporation Pegasus expendable launch vehicle, which was subsequently deployed successfully from an NB-52B airplane, on 9 Feb. 1993. The safety issues discussed in the report include command, control and communications responsibility, launch crew fatigue, launch interphone procedures, efficiency of launch constraints, and the lack of common launch documents. Safety recommendations concerning these issues were made to the Department of Transportation, the National Aeronautics and Space Administration, and the Orbital Sciences Corporation