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Aspects of Adaptive Automation Support of Air Traffic Controllers

Aspects of Adaptive Automation Support of Air Traffic Controllers. David B. Kaber, Ph.D. Department of Industrial Engineering NC State University. Introduction.

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Aspects of Adaptive Automation Support of Air Traffic Controllers

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  1. Aspects of Adaptive Automation Support of Air Traffic Controllers David B. Kaber, Ph.D. Department of Industrial Engineering NC State University NASA Langley Core Competency Directors' Visit, September 4, 2003

  2. Introduction • Adaptive automation (AA) - Dynamic allocation of machine system control to human operator or computer over time with purpose of optimizing performance (Kaber & Riley, 1999). • Example system: • Ground Collision Avoidance System in fighter aircraft (e.g., F-16) • System monitors and predicts altitude. • Provides warnings to pilot. • Takes control of flight path. • Returns control to human pilot. • Historically implemented using binary approach (Parasuraman et al., 1993; Hilburn et al., 1993):

  3. Potential solution to problems with high-level, static automation: High monitoring workload (Wiener, 1988). Operator complacency and vigilance decrements (Parasuraman et al., 1993). Loss of situation awareness (SA; Kaber et al., 2001). Skill decay (Parasuraman, 2000; Shiff, 1983). Benefits of AA found in prior research: Improvements in monitoring performance in multiple task scenario (using MAT Battery) compared to static automation (Parasuraman et al., 1993; Hilburn et al., 1993). Lower operator perceived workload in air traffic control (ATC) simulation compared to static automation (Hilburn et al., 1997). Questions remain as to how to effectively implement AA: What functions should be automated? Who invokes automation? What is basis for function allocations (operator workload, SA, etc.)? (Wickens & Hollands, 2000) Why Study AA? NASA Langley Core Competency Directors' Visit, September 4, 2003

  4. Grants and Projects Completed • FY01: • “Human Response to Adaptive Automation of Information Acquisition Functions and Later Stages of Information Processing” • NAG-1-0139; $39,971 (PM: L.J. Prinzel) • FY02: • “Authority in Adaptive Automation Applied to Various Stages of Human-Machine System Information Processing” • NAG-1-02056; $50,067 (PM: L.J. Prinzel) • NASA GSRP Grant: • “Comparison of Physiological and Secondary Task Measures for Triggering Adaptive Automation” • NGT-1-01004; $66,000 (TPOC: L.J. Prinzel) • Period of performance: 5/15/01-5/14/04. NASA Langley Core Competency Directors' Visit, September 4, 2003

  5. Need - Compare effectiveness of AA applied to various air traffic controller information processing functions, including: Information acquisition - gathering of aircraft flight parameter data. (Auto was like radar tracking system.) Information analysis - summarizing data, including conflict identification. (Auto was like futuristic EDD or TPA.) Decision making - sorting aircraft in terms of priority for clearance based on potential conflicts. (Auto was like CAA.) Action implementation - automated clearances after communication link established (auto was like data link). Approach: Developed PC-based simulation of TRACON and secondary monitoring task. ATC functions were adaptively automated based on monitoring task performance (objective measure of workload). AA of ATC Information Processing Functions Keyhole Automation Aid

  6. Findings: Performance “best” when AA applied to act of issuing clearances (action implementation). Manual control periods as part of AA of action implementation better than all other conditions and completely manual control. AA of information acquisition significantly reduced controller workload. AA of information analysis function produced highest workload. May have been due to complexity of automation aid and visual attention demands of display. Workload measure very sensitive to auto state changes when AA applied to action implementation. Workload-Matched AA of Controller Functions Performance During Manual Minutes Acquisition Action Acquisition Decision Manual Analysis Analysis Decision Action Manual

  7. FY01 Outcomes • Conference proceeding paper: • Clamann, M. P., Wright, M. C. & Kaber, D. B. (2002). Comparison of performance effects of adaptive automation applied to various stages of human-machine system information processing. In Proceedings of the 46th Annual Meeting of the Human Factors and Ergonomics Society (pp. 342-346). Santa Monica, CA: Human Factors and Ergonomics Society. • NASA Technical Publication: • Kaber, D. B., Prinzel, L. J., Wright, M. C. & Clamann, M. P. (2002). Workload-matched adaptive automation support of air traffic controller information processing stages (Tech. Pub.: NASA/TP-2002-211932). Washington, DC: NASA. NASA Langley Core Competency Directors' Visit, September 4, 2003

  8. FY01 Outcomes • Book chapter: • Kaber, D. B. & Wright, M. C. (in press). Adaptive automation of stages of information processing and the interplay with operator functional states. To appear in. G. R. J. Hockey, A. W. K. Gaillard & O. Burov (Eds.), NATO Advanced Research Workshop - Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks. Amsterdam: IOS Press, NATO Science Series. • Journal article: • Kaber, D. B., Wright, M. C. & Clamann, M. P. (in revision). Adaptive automation of information processing functions and operator stress. Submitted to Human Factors (5/31/02). NASA Langley Core Competency Directors' Visit, September 4, 2003

  9. Need - Assess human performance and workload effects of various forms of authority in AA of ATC information processing functions. Computer mandates - computer has complete control of dynamic function allocations in ATC task. Human input is irrelevant. Computer suggestion - computer initiates function allocations but needs human approval to invoke. Approach: Developed new version of ATC simulation including feedback display on locus of control and automation. Implemented same forms of ATC function automation as studied in FY01 (info acquisition, info analysis, decision making, action implementation). All computer mandates and suggestions for automation based on operator secondary task performance (workload). Authority in AA of ATC Functions Automation Feedback Display

  10. Findings: Automation of data gathering function (information acquisition) yielded best performance. Automation of sorting of aircraft for clearance (decision making) produced worst performance. Performance hindered by complexity of automation. High visual display demand. Computer suggestions of automation better than mandates, in general. However, when performing task manually, mandates of auto better than suggestions. No differences in workload among types of automation of forms of authority. Invocation Authority in AA

  11. Masters thesis: Clamann, M. P. (2002). “The Effects of Intermediate Levels of Invocation Authority on Adaptive Automation of Various Stages of Information Processing.” Technical Report: Kaber, D. B. & Clamann, M. P. (March 2003). Authority in adaptive automation applied to various stages of human-machine system information processing. (Final Rep.: NASA Langley Research Center Grant #NAG-1-02056). Hampton, VA: NASA Langley Research Center. Conference proceeding paper: Clamann, M. P. & Kaber, D. B. (in press). Authority in adaptive automation applied to various stages of human-machine system information processing. To appear in Proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society. Santa Monica, CA: Human Factors and Ergonomics Society. FY02 Outcomes NASA Langley Core Competency Directors' Visit, September 4, 2003

  12. Current Research: SA and AA • FY03: • “A Situation Awareness-Based Approach to Adaptive Automation” • NAG-1-03022; $100,000 (PM: L.J. Prinzel) • Subcontract to SA Technologies, Inc. for consultation on development of real-time measure of SA ($9,984). • Needs: • Define measure of SA in ATC sensitive to dynamic function allocations as part of AA. • Empirically assess utility of controller SA measure for classifying forms of AA of info processing functions. • Describe impact of AA of ATC information processing functions on controller SA. NASA Langley Core Competency Directors' Visit, September 4, 2003

  13. Approach: Developed enhanced version of TRACON simulation based on input from PM. Studied task analyses on en route control and TRACON (e.g., Endsley & Rodgers, 1994; Endsley & Jones, 1995). Developed query-based measure of controller SA (like Situation Awareness Global Assessment Technique (Endsley, 1995)). Currently conducting experiment: Eight trained subjects working over 3 week period. Experience all forms of AA of ATC task. SA quizzes posed during experimental trials: Simulation frozen at random points in time. Subjects respond to questions on state of aircraft and environment. Responses compared with actual state of task to evaluate to accuracy of SA. Hypotheses: Lower levels of automation, including information acquisition and action implementation expected to support controller SA. Manual control periods as part of AA expected to increase controller SA. Information analysis automation may degrade system state comprehension. Decision making auto may degrade operator projection of future system states. SA and AA

  14. Enhanced ATC Simulation Trajectory Projection Aid. Radar scan line. Data box (flight parameters). Control box for aircraft clearances. Command history and feedback. Automation aid & status display. Aircraft being cleared. NASA Langley Core Competency Directors' Visit, September 4, 2003

  15. FY03 Outcomes • Masters thesis: • McClernon, C. K. (in preparation). “Situation Awareness Effects of Adaptive Automation of Various Air Traffic Control Information Processing Functions” • Journal Article: • Kaber, D. B. & Endsley, M. R. (in press). The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Submitted to Theoretical Issues in Ergonomics Science (12/15/01). NASA Langley Core Competency Directors' Visit, September 4, 2003

  16. Future Research • Develop real-time probe measure of controller SA for use in research on AA. • Conduct experiment involving real-time assessment of controller SA as basis for triggering dynamic function allocations (manual and automated control allocations). • Compare results of workload-matched AA and SA-matched AA support on controller information processing. • Assess performance, workload and SA effects of AA of multiple ATC information processing functions, simultaneously. NASA Langley Core Competency Directors' Visit, September 4, 2003

  17. David B. Kaber, Ph.D. Associate Professor Department of Industrial Engineering North Carolina State University 2401 Stinson Dr. 328 Riddick Labs Box 7906 Raleigh, NC 27695-7906 Tel.: (919) 515-2362 FAX: (919) 515-5281 e-mail: dbkaber@eos.ncsu.edu Faculty Web Page: http://www.ie.ncsu.edu/kaber/ FY01 Results Page: http://people.engr.ncsu.edu/dbkaber/AA/ Contact and Web Site Information NASA Langley Core Competency Directors' Visit, September 4, 2003

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