Becky L. Hooey

Human performance modeling in aviation

Goals, Aviation Problems, and Modeling The NASA Human Performance Modeling Project: Goals, Approach, and Overview, D.C. Foyle and B.L. Hooey Using Human Performance Modeling in Aviation, D.C. Foyle and B.L. Hooey Aviation Safety Studies: Taxi Navigation Errors and Synthetic Vision System Operations, B.L. Hooey and D.C. Foyle Application of Individual Modeling Tools to the Aviation Problems Overview of Human Performance Modeling Tools, K. Leiden, M.D. Byrne, K.M. Corker, S.E. Deutsch, C. Lebiere, and C.D. Wickens An ACT-R Approach to Closing the Loop on Computational Cognitive Modeling: Describing Dynamics of Interactive Decision Making and Attention Allocation, M.D. Byrne, A. Kirlik, and M.D. Fleetwood Modeling Pilot Performance With an Integrated Task Network and Cognitive Architecture Approach, C. Lebiere, R. Archer, B. Best, and D. Schunk Air MIDAS: A Closed-Loop Model Framework, K.M. Corker, K. Muraoka, S. Verma, A. Jadhav, and B.F. Gore D-OMAR: An Architecture for Modeling Multitask Behaviors, S.E. Deutsch and R.W. Pew Attention-Situation Awareness (A-SA) Model of Pilot Error, C.D. Wickens, J.S. McCarley, A.L. Alexander, L.C. Thomas, M. Ambinder, and S. Zheng Implications for Modeling and Aviation A Cross-Model Comparison, K. Leiden and B. Best Human Performance Modeling: A Virtual Roundtable Discussion, D.C. Foyle, B.L. Hooey, M.D. Byrne, A. Kirlik, C. Lebiere, R. Archer, K.M. Corker, S.E. Deutsch, R.W. Pew, C.D. Wickens, and J.S. McCarley Advancing the State of the Art of Human Performance Models to Improve Aviation Safety, B.L. Hooey and D.C. Foyle Index

Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions

Objective: The objective is to validate a computational model of visual attention against empirical data—derived from a meta-analysis—of pilots’ failure to notice safety-critical unexpected events. Background: Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing— salience, expectancy, effort, and value), was developed to predict these failures. Method: First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. Results: The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. Conclusion: The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. Applications: As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots’ failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

Integration of Cockpit Displays for Surface Operations: The Final Stage of a Human-Centered Design Approach

A suite of cockpit navigation displays for low-visibility airport surface operations has been designed by researchers at NASA Ames Research Center following a human-centered process. This paper reports on the final research effort in this process that examined the procedural integration of these technologies into the flight deck. Using NASA Ames’ high-fidelity Advanced Concepts Flight Simulator, eighteen airline crews completed fourteen low-visibility (RVR 1000’) land-andtaxi scenarios that included both nominal (i.e., hold short of intersections, route amendments) and off-nominal taxi scenarios designed to assess how pilots integrate these technologies into their procedures and operations. Recommendations for integrating datalink and cockpit displays into current and future surface operations are provided.

AN EVALUATION OF THE TAXIWAY NAVIGATION AND SITUATION AWARENESS (T-NASA) SYSTEM IN HIGH-FIDELITY SIMULATION

The effects of an electronic moving map and a HUD on ground taxi performance in reduced visibility were examined in a high-fidelity simulation. Sixteen commercial flight crews completed 21 trials, each consisting of an autoland arrival to Chicago O’Hare and taxi to an apron area. Relative to a baseline (paper-chart only) condition, the EMM/HUD combination increased forward speed by 21%, and reduced navigation errors by nearly 100%. These results, together with workload ratings, situation awareness ratings, analyses of crew interactions, and pilot feedback, provide strong evidence that the combination of head-up symbology and an EMM can substantially improve both the efficiency and the safety of ground operations.

Modeling Pilot Situation Awareness

Introduction The Man–machine Integration Design and Analysis (MIDAS) human performance model was augmented to improve predictions of multi-operator situation awareness (SA). In MIDAS, the environment is defined by situation elements (SE) that are processed by the modeled operator via a series of sub-models including visual attention, perception, and memory. Collectively, these sub-models represent the situation assessment process and determine which SEs are attended to, and comprehended by, the modeled operator. SA is computed as a ratio of the Actual SA (the number of SEs that are detected or comprehended) to the Optimal SA (the number of SEs that are required or desired to complete the task).

A Computational Implementation of a Human Attention Guiding Mechanism in MIDAS v5

In complex human-machine systems, the human operator is often required to intervene to detect and solve problems. Given this increased reliance on the human in these critical human-machine systems, there is an increasing need to validly predict how operators allocate their visual attention. This paper describes the information-seeking (attention-guiding) model within the Man-machine Integration Design and Analysis System (MIDAS) v5 software - a predictive model that uses the Salience, Effort, Expectancy and Value (SEEV) of an area of interest to guide a persons attention. The paper highlights the differences between using a probabilistic fixation approach and the SEEV approach in MIDAS to drive attention.

Pilot Navigation Errors on the Airport Surface: Identifying Contributing Factors and Mitigating Solutions

A taxonomy of navigation errors (pilot deviations) during taxi operations was developed that defines 3 classes of errors: planning, decision, and execution errors. This taxonomy was applied to error data from 2 full-mission simulation studies (Hooey, Foyle, Andre, & Parke, 2000; McCann et al., 1998) that included trials that replicated current-day operations and trials with advanced cockpit technologies including datalink, electronic moving maps (EMM), and head-up displays (HUDs). Pilots committed navigation error s on 17% of current-day operations trials (in low-visibility and night), distributed roughly equally across the 3 error classes. Each error class was associated with a unique set of contributing factor s and mitigating solutions. Planning errors were mitigated by technologies that provided an unambiguous record of the clearance (datalink and the EMM, which possessed a text-based clearance). Decision errors were mitigated by technologies that provided both local and global awareness including information about the distance to and direction of the next turn, current township location, and a graphical depiction of the route (as provided by the EMM and HUD together ). Execution errors were best mitigated by the HUD, which disambiguated the environment and depicted the clear ed taxi route. Implications for technology design and integration are provided.

Field evaluation of T-NASA: taxi navigation and situation awareness system

This paper reports the results of a field evaluation of an advanced taxi navigation and situation awareness (T-NASA) system, aimed at improving the efficiency of aircraft ground taxi operations under low-visibility conditions. T-NASA consists of two main components: 1) a panel-mounted electronic taxi map display and 2) a heads-up scene-linked display (HUD). These components were installed in NASAs B-757 research aircraft and flight tested at Atlantas Hartsfield International Airport, The results clearly demonstrated both the feasibility and effectiveness of the T-NASA system towards improving the efficiency of airport taxi operations, In addition, as a direct result of the evaluation, improvements were made to the design and procedures of the T-NASA system.

Meaningful Assessments of Simulator Performance and Sickness: Can't Have One without the Other?

The requirement to evaluate the differential impacts of simulator sickness on performance assessments was explored. Simulator sickness and performance data were analyzed in two phases that indicated: 1) an experimental display condition by age interaction with regard to development of simulator sickness; and 2) associated detrimental effects of simulator sickness on performance. Arguably, these results may be quite disturbing to users, and past users, of simulators for system and other Development, Test and Evaluation efforts (DT&Es). The utility of simulator sickness measures as covariates, in the analysis of performance effects, is demonstrated as a means for their assessment and statistical control. It is strongly recommended that researchers explore and control the potential confounding effects of simulator sickness in order to assure meaningful performance assessments.

4-D Taxi Clearances: Pilots' Usage of Time- and Speed-Based Formats

Automated surface management systems are being developed that utilize dynamic algorithms to calculate the most efficient movement of all surface traffic in order to increase the efficiency with which airport surfaces are utilized. If these systems are to be implemented, pilots will be required to comply with 4-D taxi clearances, in which a pilot is required to be at a specific location at a specific time. This pilot-in-the-loop simulation study is an investigation of how to present the information necessary for pilots to comply with such 4-D taxi clearances. This study is aimed at determining the fundamental abilities that pilots have in complying with clearances given as time-based versus speed-based commands, without the use of an advanced display. Using a medium-fidelity surface operations simulator at NASA Ames Research Center, with Boeing 737 modeled dynamics, 18 commercial airline Captains each completed 45 taxi clearance scenarios. Each taxi clearance scenario required pilots to follow a cleared taxi route and to make an active runway crossing at a specific future time by following either a time-based or speed-based clearance. Taxi clearances were presented using: 1) a Speed format that displayed current ground speed and a commanded average ground speed; 2) a Time format that displayed elapsed time and a commanded time of arrival; or, 3) a Speed/time format that displayed all of the information from the Speed and Time formats simultaneously. Pilots’ time-of-arrival (TOA) absolute error, TOA error bias, and velocity standard deviation (SD) were recorded for each trial. Overall, the results suggest that having both speed and time information provides more accurate and less variable 4-D clearance compliance. Additionally, the results indicate that utilizing shorter taxi clearances can maximize TOA accuracy. This baseline study indicates that the development of flight deck displays for 4-D taxi clearances may likely require the presentation of both time-based information and speed-based information for accurate and efficient use by the flight crew.