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Automation
and
Autonomy
Lisa Jo Elliott, Ph.D.
University of South Florida
Automation
“any sensing, detection,
information-processing, decision-
making, or control action that could
be performed by humans but is
actually performed by machine.”
- Moray et al. (2000, p. 44)
System designers promise automation
delivers…
• decreased workload
• increased precision
• better system performance.
System operators find automation delivers…
• system failures
• automation-induced accidents
• imperfect team member
UAS vs. Manned Flight Operation
• Automation is one of the few areas.
– Manned studies translate to unmanned systems
• Depending on the size and use of the UAS
– Smaller vehicles less automation
• Role of automation is well defined (waypoint
navigation).
– Larger systems complex automation along with
several team members and several roles.
Automation has Behavior
• Interacts with the operator’s behavior
– mental model of the system
– trust of the system.
• Automation changes an operator’s
– training,
– task assignments,
– workload,
– situation awareness,
– trust
– operator’s skill set
Automation is not
an equal team member
• Changes the dynamics between the operator
& the system.
– automation is limited in its ability
– automation is deaf,
– cannot freely communicate
– only the abilities that the system designer has
deemed necessary.
Re-Distributing Workload from Operators
to the Automation
• Often, aids appear at precisely the time when
workload is the greatest.
• Operators do not have the time to set up the
automation in addition to flying the plane.
• In order for automation to relieve workload
it must be incorporated gradually and before
operator workload is at its maximum.
Skill Decrement
???!
Trust
• “automation etiquette guidelines”
– Based on Gricean Maxims
• Can overcome low automation reliability:
– performance in the low-reliability/good-etiquette
condition was almost as good as …
– that in the high reliability/poor etiquette
condition.
Miller et al., 2004; Sheridan and Parasuraman ,2006, (p. 103).
Automation is not All or None
• The amount of automation and the roles it plays are dictated by
– system size
– number of operators
– mission.
• Static automation is hard-wired.
– The system designer chooses whom (the system or the operator) and
how (manual or automatic) a task will be performed.
• Adaptive automation is called up by an operator event.
– explicit (a request for aid)
– implicit (tied to operator workload)
– situational event (takeoff speed).
– ability to turn itself on or off.
McCarley and Wickens, 2005
Taxonomies
• The human-centered taxonomies are useful
– isolating operator issues
– system performance issues
– defining what the automation can and should be
doing in terms of human cognitive performance
Wickens, 2008a
Two types of Taxonomies
• Functional Centered Taxonomies
• Technocentric Taxonomies
Functional Taxonomy:
Four Classes of Automation Functions
• 1) information acquisition;
• (2) information analysis;
• (3) decision and action selection;
• (4) action implementation
• Parasuraman and Colleagues (2000)
Parasuraman et al., 2000, p. 288
Technocentric Taxonomy: NASA FLOAAT
• Function-Specific Level of Autonomy and Automation
Tool (FLOAAT) to facilitate its system requirements
development.
• lowest level of automation states that
– all data monitoring
– calculation
– decisions
– tasks are executed by the ground station.
• highest level of automation moves
– all data monitoring, calculation, decisions, and task
execution to the onboard system without any assistance
from the human..
Proud and Hart, 2005
Ongoing Challenges
• Sensory System Integration
– Inability of autonomous systems to create
consistent object representations from a variety of
sensors.
• System communication with an operator
– in case of system failure
• Intra System issues of
– Trust, situation awareness, & workload.
References
Billings, C. E., and D. D. Woods. 1994.Concerns about adaptive automation in aviation systems. In Human Performance in Automated Systems: Current
Research and Trends, ed. M. Mouloua and R. Parasuraman, 24–29. Hillsdale, NJ: Erlbaum.
Heintz, F., and P. Doherty. 2004. DyKnow: An approach to middleware for knowledge processing. Journal of Intelligent and Fuzzy Systems 15: 3–13.
Lee, J. D., and K. A. See. 2004. Trust in automation and technology: Designing for appropriate reliance. Human Factors 46: 50–80.
McCarley, J. S., and C. D. Wickens. 2005. Human factors implications of UAVs in the national airspace (Technical report AHFD-05-05/FAA-05-01).
Aviation Human Factors Division, Savoy, Illinois.
Miller, C. A., Goldman, R. P. and Funk, H. B. 2004. Delegation approaches to multiple unmanned vehicle control. Proceedings of the Workshop on
Human Factors of Unmanned Aerial Vehicles: Manning the Unmanned CERI, Tempe, AZ.
Moray, N., T. Inagaki, and M. Itoh. 2000. Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of
Experimental Psychology: Applied 6: 44–58.
Pettersson, P. O., and P. Doherty. 2004. Probabilistic roadmap based planning for an autonomous unmanned helicopter. Sensors: 1–6.
Parasuraman, R., and C. A. Miller. 2004. Trust and etiquette in high-criticality automated systems. Communications of the ACM 47: 51–55.
Parasuraman, R., and V. A. Riley. 1997. Humans and automation: Use, misuse, disuse and abuse. Human Factors 39: 230–253.
Parasuraman, R., T. B. Sheridan, and C. D. Wickens. 2000. A model for types and levels of human interaction with automation. IEEE Transactions on
Systems, Man and Cybernetics—Part A: Systems and Humans 30: 286–297.
Parasuraman, R., and C. D. Wickens. 2008. Humans: Still vital after all these years of automation. Human Factors 50: 511–520.
Proud, R. W., and J. J. Hart (2005). FLOAAT: A Tool for Determining Levels of Autonomy and Automation, Applied to Human-Rated Space Systems. AIAA
infotech@Aerospace 2005. Arlington, VA: American Institute of Aeronautics and Astronautics.
Sarter, N., D. D. Woods, and C. E. Billings. 1997. Automation surprises. In Handbook of Human Factors and Ergonomics (2nd ed.), ed. G. Salvendy, 1926–
1943. New York: Wiley.
Sheridan, T., and R. Parasuraman. 2006. Human-automation interaction. In Reviews of Human Factors and Ergonomics, ed. R. S. Nickerson, Vol. 1, 89–
129. Santa Monica, CA: Human Factors and Ergonomics Society.
Wickens, C. D. 2008a. Functional allocation and the degree of automation. Presentation at the Rocky Mountain
Chapter of HFES. http://Function_allocation_and_the_degree_of_automation_C_Wickens_16_Oct_2008.pdf
(accessed June 6, 2010).
Wickens, C. D. 2008b. Situation awareness: Review of Mica Endsley’s 1995 articles on SA theory and measurement.
Human Factors 50: 397–403.

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Hf in uas auto

  • 1. Automation and Autonomy Lisa Jo Elliott, Ph.D. University of South Florida
  • 2. Automation “any sensing, detection, information-processing, decision- making, or control action that could be performed by humans but is actually performed by machine.” - Moray et al. (2000, p. 44)
  • 3. System designers promise automation delivers… • decreased workload • increased precision • better system performance. System operators find automation delivers… • system failures • automation-induced accidents • imperfect team member
  • 4. UAS vs. Manned Flight Operation • Automation is one of the few areas. – Manned studies translate to unmanned systems • Depending on the size and use of the UAS – Smaller vehicles less automation • Role of automation is well defined (waypoint navigation). – Larger systems complex automation along with several team members and several roles.
  • 5. Automation has Behavior • Interacts with the operator’s behavior – mental model of the system – trust of the system. • Automation changes an operator’s – training, – task assignments, – workload, – situation awareness, – trust – operator’s skill set
  • 6. Automation is not an equal team member • Changes the dynamics between the operator & the system. – automation is limited in its ability – automation is deaf, – cannot freely communicate – only the abilities that the system designer has deemed necessary.
  • 7. Re-Distributing Workload from Operators to the Automation • Often, aids appear at precisely the time when workload is the greatest. • Operators do not have the time to set up the automation in addition to flying the plane. • In order for automation to relieve workload it must be incorporated gradually and before operator workload is at its maximum.
  • 9. Trust • “automation etiquette guidelines” – Based on Gricean Maxims • Can overcome low automation reliability: – performance in the low-reliability/good-etiquette condition was almost as good as … – that in the high reliability/poor etiquette condition. Miller et al., 2004; Sheridan and Parasuraman ,2006, (p. 103).
  • 10. Automation is not All or None • The amount of automation and the roles it plays are dictated by – system size – number of operators – mission. • Static automation is hard-wired. – The system designer chooses whom (the system or the operator) and how (manual or automatic) a task will be performed. • Adaptive automation is called up by an operator event. – explicit (a request for aid) – implicit (tied to operator workload) – situational event (takeoff speed). – ability to turn itself on or off. McCarley and Wickens, 2005
  • 11. Taxonomies • The human-centered taxonomies are useful – isolating operator issues – system performance issues – defining what the automation can and should be doing in terms of human cognitive performance Wickens, 2008a
  • 12. Two types of Taxonomies • Functional Centered Taxonomies • Technocentric Taxonomies
  • 13. Functional Taxonomy: Four Classes of Automation Functions • 1) information acquisition; • (2) information analysis; • (3) decision and action selection; • (4) action implementation • Parasuraman and Colleagues (2000) Parasuraman et al., 2000, p. 288
  • 14. Technocentric Taxonomy: NASA FLOAAT • Function-Specific Level of Autonomy and Automation Tool (FLOAAT) to facilitate its system requirements development. • lowest level of automation states that – all data monitoring – calculation – decisions – tasks are executed by the ground station. • highest level of automation moves – all data monitoring, calculation, decisions, and task execution to the onboard system without any assistance from the human.. Proud and Hart, 2005
  • 15. Ongoing Challenges • Sensory System Integration – Inability of autonomous systems to create consistent object representations from a variety of sensors. • System communication with an operator – in case of system failure • Intra System issues of – Trust, situation awareness, & workload.
  • 16. References Billings, C. E., and D. D. Woods. 1994.Concerns about adaptive automation in aviation systems. In Human Performance in Automated Systems: Current Research and Trends, ed. M. Mouloua and R. Parasuraman, 24–29. Hillsdale, NJ: Erlbaum. Heintz, F., and P. Doherty. 2004. DyKnow: An approach to middleware for knowledge processing. Journal of Intelligent and Fuzzy Systems 15: 3–13. Lee, J. D., and K. A. See. 2004. Trust in automation and technology: Designing for appropriate reliance. Human Factors 46: 50–80. McCarley, J. S., and C. D. Wickens. 2005. Human factors implications of UAVs in the national airspace (Technical report AHFD-05-05/FAA-05-01). Aviation Human Factors Division, Savoy, Illinois. Miller, C. A., Goldman, R. P. and Funk, H. B. 2004. Delegation approaches to multiple unmanned vehicle control. Proceedings of the Workshop on Human Factors of Unmanned Aerial Vehicles: Manning the Unmanned CERI, Tempe, AZ. Moray, N., T. Inagaki, and M. Itoh. 2000. Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of Experimental Psychology: Applied 6: 44–58. Pettersson, P. O., and P. Doherty. 2004. Probabilistic roadmap based planning for an autonomous unmanned helicopter. Sensors: 1–6. Parasuraman, R., and C. A. Miller. 2004. Trust and etiquette in high-criticality automated systems. Communications of the ACM 47: 51–55. Parasuraman, R., and V. A. Riley. 1997. Humans and automation: Use, misuse, disuse and abuse. Human Factors 39: 230–253. Parasuraman, R., T. B. Sheridan, and C. D. Wickens. 2000. A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans 30: 286–297. Parasuraman, R., and C. D. Wickens. 2008. Humans: Still vital after all these years of automation. Human Factors 50: 511–520. Proud, R. W., and J. J. Hart (2005). FLOAAT: A Tool for Determining Levels of Autonomy and Automation, Applied to Human-Rated Space Systems. AIAA infotech@Aerospace 2005. Arlington, VA: American Institute of Aeronautics and Astronautics. Sarter, N., D. D. Woods, and C. E. Billings. 1997. Automation surprises. In Handbook of Human Factors and Ergonomics (2nd ed.), ed. G. Salvendy, 1926– 1943. New York: Wiley. Sheridan, T., and R. Parasuraman. 2006. Human-automation interaction. In Reviews of Human Factors and Ergonomics, ed. R. S. Nickerson, Vol. 1, 89– 129. Santa Monica, CA: Human Factors and Ergonomics Society. Wickens, C. D. 2008a. Functional allocation and the degree of automation. Presentation at the Rocky Mountain Chapter of HFES. http://Function_allocation_and_the_degree_of_automation_C_Wickens_16_Oct_2008.pdf (accessed June 6, 2010). Wickens, C. D. 2008b. Situation awareness: Review of Mica Endsley’s 1995 articles on SA theory and measurement. Human Factors 50: 397–403.