A theoretical framework for designing human-centered automotive automation systems

Abstract

Increasingly sophisticated and robust automotive automation systems are being developed to be applied in all aspects of driving. Benefits, such as improving safety, task performance, and workload have been reported. However, several critical accidents involving automation assistance have also been reported. Although automation systems may work appropriately, human factors such as drivers errors, overtrust in and overreliance on automation due to lack of understanding of automation functionalities and limitations as well as distrust caused by automation surprises may trigger inappropriate human–automation interactions that lead to negative consequences. Several important methodologies and efforts for improving human–automation interactions follow the concept of human-centered automation, which claims that the human must have the final authority over the system, have been called. Given that the human-centered automation has been proposed as a more cooperative automation approach to reduce the likelihood of human–machine misunderstanding. This study argues that, especially in critical situations, the way control is handed over between agents can improve human–automation interactions even when the system has the final decision-making authority. As ways of improving human–automation interactions, the study proposes adaptive sharing of control that allows dynamic control distribution between human and system within the same level of automation while the human retains the final authority, and adaptive trading of control in which the control and authority shift between human and system dynamically while changing levels of automation. Authority and control transitions strategies are discussed, compared and clarified in terms of levels and types of automation. Finally, design aspects for determining how and when the control and authority can be shifted between human and automation are proposed with recommendations for future designs.

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References

  1. Abbink DA, Boer ER, Mulder M (2008) Motivation for continuous haptic gas pedal feedback to support car following. In: Intelligent vehicles symposium, 2008 IEEE, pp 283–290

  2. Abbink DA, Mulder M, Boer ER (2012) Haptic shared control: smoothly shifting control authority?. Cogn Technol Work 14:19–28

    Google Scholar 

  3. Baddeley A (2012) Working memory: theories, models, and controversies. Annu Rev Psychol 63:1–29

    Google Scholar 

  4. Bainbridge L (1983) Ironies of automation. Automatica 19:775–780

    Google Scholar 

  5. Billings CE (1997) Aviation automation: the search for a human-centered approach. Lawrence Erlbaum Associates, Mahwah

    Google Scholar 

  6. Blaschke C, Breyer F, Farber B, Freyer J, Limbacher R (2009) Driver distraction based lane-keeping assistance. Transport Res Part F Traffic Psychol Behav 12(4):288–299

    Google Scholar 

  7. Coelingh E, Eidehall A, Bengtsson M (2010) Collision warning with full auto brake and pedestrian detection—a practical example of automatic emergency braking. In: Proceedings of 2010 13th international IEEE annual conference on intelligent transportation systems, Madeira Island, Portugal, pp 155–160. https://doi.org/10.1109/ITSC.2010.5625077

  8. Corona D, Schutter BD (2008) Adaptive cruise control for a smart car: a comparison benchmark for MPC-PWA control methods. Trans Control Syst Technol 16(2):365–372

    Google Scholar 

  9. Dekker SW, Woods DD (2002) Maba-Maba or abracadabra? Progress on human–automation coordination. Cogn Technol Work 4(4):1–13

    Google Scholar 

  10. Dickmanns ED (2002) Vision for ground vehicles: history and prospects. Int J Veh Auton Syst 1(1):1–44

    Google Scholar 

  11. Dingus TA, Klauer SG, Neale VL, Petersen A, Lee SE, Sudweeks J, Knipling RR (2006) The 100-car naturalistic driving study: Phase II—results of the 100-car field experiment. Report DOT-HS-810-593. National Highway Traffic Safety Administration, Washington, D.C.

    Google Scholar 

  12. Endsley MR, Kiris EO (1995) The out-of-the-loop performance problem and level of control in automation. Hum Factors 37:381–394

    Google Scholar 

  13. Flemisch FO, Kelsch J, Löper C, Schieben A, Schindler J (2008) Automation spectrum, inner/outer compatibility and other potentially useful human factors concepts for assistance and automation. In: de Waard D, Flemisch FO, Lorenz B, Oberheid H, Brookhuis KA (eds) Human factors for assistance and automation, no. 2008. Shaker Publishing, Maastricht, pp 1–16

    Google Scholar 

  14. Flemisch FO, Heesen M, Hesse T, Kelsch J, Schieben A, Beller J (2012) Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn Technol Work 14(1):3–18. https://doi.org/10.1007/s10111-011-0191-6

    Article  Google Scholar 

  15. Gao J, Lee JD, Zhang Y (2006) A dynamic model of interaction between reliance on automation and cooperation in multi-operator multi-automation situations. Int J Ind Ergon 36(5):511–526

    Google Scholar 

  16. Green M (2003) Skewed view: accident investigation. Occup Health Saf Can 2003:24–29

    Google Scholar 

  17. Griffiths PG, Gillespie RB (2005) Sharing control between humans and automation using haptic interface: primary and secondary task performance benefits. Hum Factors J Hum Factors Ergon Soc 47(3):574–590

    Google Scholar 

  18. Ho WL, Cummings ML, Wang E, Tijerina L, Kochhar DS (2006) Integrating intelligent driver warning systems: effects of multiple alarms and distraction on driver performance. TRB Annual Meeting

  19. Inagaki T (2000) Situation-adaptive autonomy for time critical takeoff decisions. Int J Modell Simul 20(2):175–180

    Google Scholar 

  20. Inagaki T (2003) Adaptive automation: sharing and trading of control. In: Hollnagel E (ed) Handbook of cognitive task design. Erlbaum, Mahwah, pp 147–169

    Google Scholar 

  21. Inagaki T (2006) Design of human–machine interactions in light of domain-dependence of human-centered automation. Cogn Technol Work 8(3):161–167

    Google Scholar 

  22. Inagaki T (2008) Smart collaboration between humans and machines based on mutual understanding. Annu Rev Control 32(2):253–261

    Google Scholar 

  23. Inagaki T (2011) To what extent may assistance systems correct and prevent ‘erroneous’ behaviour of the driver? In: Cacciabue PC (eds) Human modelling in assisted transportation. Springer, Milan, pp 33–41

    Google Scholar 

  24. Inagaki T, Itoh M (2013) Human’s overtrust in and overreliance on advanced driver assistance systems: a theoretical framework. Int J Veh Technol 2013:8 (Article ID 951762)

    Google Scholar 

  25. Inagaki T, Sheridan TB (2012) Authority and responsibility in human–machine systems: probability theoretic validation of machine-initiated trading of authority. Cogn Technol Work 14(1):29–37

    Google Scholar 

  26. Inagaki T, Moray N, Itoh M (1998) Trust, self-confidence and authority in humanmachine systems. In: Proceedings of the IFAC man–machine systems, pp 431–436

    Google Scholar 

  27. Inagaki T, Itoh M, Nagai Y (2007) Support by warning or by action: which is appropriate under mismatches between driver intent and traffic conditions?. IEICE Trans Fundam Electron Commun Comput Sci E90-A(11):2540–2545

    Google Scholar 

  28. Itoh M (2012) Toward overtrust-free advanced driver assistance systems. Cogn Technol Work 14(1):51–60

    Google Scholar 

  29. Itoh M, Inagaki T (2014) Design and evaluation of steering protection for avoiding collisions during a lane change. Ergonomics 57(3):361–373

    Google Scholar 

  30. Itoh M, Horikome T, Inagaki T (2013) Effectiveness and driver acceptance of a semi-autonomous forward obstacle collision avoidance system. Appl Ergon 44:756–763

    Google Scholar 

  31. Kaber DB (2017) Issues in human–automation interaction modeling: presumptive aspects of frameworks of types and levels of automation. J Cogn Eng Decis Mak 12(1):7–24

    Google Scholar 

  32. Kaber DB, Endsley MR (2003) The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theor Issues Ergon Sci 5(2):113–153

    Google Scholar 

  33. Kanaris A, Kosmatopoulos EB, Ioaunou PA (2001) Strategies and spacing requirements for lane changing and merging in automated highway systems. IEEE Trans Veh Technol 50(6):1568–1581

    Google Scholar 

  34. Katja K, Annika L, Jonas AH (2014) Tactical driving behavior with different levels of automation. IEEE Trans Intell Transp Syst 15(1):158–167

    Google Scholar 

  35. Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46(1):50–80

    Google Scholar 

  36. Lee JD, McGehee DV, Brown TL, Marshall D (2006) Effects of adaptive cruise control and alert modality on driver performance 1980. Transp Res Rec. https://doi.org/10.3141/1980-09

    Article  Google Scholar 

  37. Llaneras RE, Salinger J, Green CA (2015) Human factors issues associated with limited ability autonomous driving systems: driver’s allocation of visual attention to the forward roadway. In: Proceedings of the 7th international driving symposium on human factors in driver assessment, training, and vehicle design, 25, pp 52–55

  38. Luo Y, Remillard J, Hoetzer D (2010) Pedestrian detection in near-infrared night vision system. Intelligent vehicles symposium (IV) IEEE2010, ISSN 1931-0587, pp 51–58

  39. Mannering F, Washburn S, Kilareski W (2009) Principles of highway engineering and traffic analysis, 4th edn. Wiley, New York

    Google Scholar 

  40. Mars F, Deroo M, Hoc JM (2014) Analysis of human–machine cooperation when driving with different degrees of haptic shared control. IEEE Trans Haptics 7(3):324–333

    Google Scholar 

  41. Merat N, Lee JD (2012) Preface to the special section on human factors and automation in vehicles: designing highly automated vehicles with the driver in mind. Hum Factors J Hum Factors Ergon Soc 54:681–686

    Google Scholar 

  42. Miller C (2005) Using delegation as an architecture for adaptive automation. Technical Report SIFT-TR 05-TUSC-01

  43. Miller C, Parasuraman R (2007) Designing for flexible interaction between humans and automation: delegation interfaces for supervisory control. Hum Factors 49:57–75

    Google Scholar 

  44. Moray N, Inagaki T, Itoh M (2000) Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. J Exp Psychol Appl 6(1):44–58

    Google Scholar 

  45. Muslim H, Itoh M (2017) Haptic shared guidance and automatic cooperative control assistance system: performance evaluation for collision avoidance during hazardous lane changes. SICE J Control Meas Syst Integr 10(5):460–467

    Google Scholar 

  46. Norman DA (1990) The ‘problem’ with automation: inappropriate feedback and interaction, not ‘over-automation’. Human factors in hazardous situations. Philos Trans R Soc Lond Ser B Biol Sci 327(1241):585–593

    Google Scholar 

  47. Pacaux-Lemoine MP, Itoh M (2015) Towards vertical and horizontal extension of shared control concept. IEEE international conference on systems, man, and cybernetics (IEEE SMC), pp 3086–3091

  48. Parasuraman R, Manzey DH (2010) Complacency and bias in human use of automation. Atten Integr Hum Factors 52(3):381–410

    Google Scholar 

  49. Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors 39:230–253

    Google Scholar 

  50. Parasuraman R, Bahri T, Deaton JE, Morrison JG, Barnes M (1992) Theory and design of adaptive automation in aviation systems. Progress Rep. No. NAWCADWAR-92033-60. Naval Air Development Center Aviation Division, Warminster

  51. Parasuraman R, Molloy R, Singh IL (1993) Performance consequences of automation-induced ‘complacency’. Int J Aviat Psychol 3(1):1–23

    Google Scholar 

  52. Parasuraman R, Sheridan TB, Wickens CD (2000) A model for the types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum 30(3):286–297

    Google Scholar 

  53. Prinzel LT III, Freeman FG, Scerbo MW, Mikulka PJ, Pope AT (2003) Effects of a psychophysiological system form adaptive automation on performance, workload, and the event-related potential P300 component. Hum Factors 45(4):601–613

    Google Scholar 

  54. SAE J3016 On-Road Automated Vehicle Standards Committee (2016) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE International, Warrendale

    Google Scholar 

  55. Salvucci DD, Liu A (2002) The time course of a lane change: driver control and eye-movement behavior. Transp Res Part F 5:123–132

    Google Scholar 

  56. Sarter NB, Woods DD, Billings CE (1997) Automation surprises. In: Salvendy G (ed) Handbook of human factors and ergonomics, 2nd ed. Wiley, Oxford, pp 1926–1943

    Google Scholar 

  57. Sheridan TB (1992) Telerobotics, automation, and human supervisory control. MIT Press, Cambridge

    Google Scholar 

  58. Sheridan TB (1995) Human centered automation: oxymoron or common sense?. In: Systems, man and cybernetics, 1995. Intelligent systems for the 21st century, IEEE international conference on, vol 1, pp 823–828

  59. Sotelo MAL, Parra I, Fernandez D, Naranjo E (2006) Pedestrian detection using SVM and multi-feature combination. Intelligent transportation systems ITS, IEEE, pp 103–108

  60. Treat JR (1977) Tri-level study of the causes of traffic accidents: an overview of final results. In: Proceedings of American Association for Automotive Medicine Annual Conference, 21, pp 391–403

  61. Van der Wiel DW, van Paassen MM, Mulder M, Mulder M, Abbink DA (2015) Driver adaptation to driving speed and road width: exploring parameters for designing adaptive haptic shared control. In: Systems, man, and cybernetics (SMC), 2015 IEEE international conference, pp 3060–3065

  62. Wickens CD, Hollands JG, Banbury S, Parasuraman R (2015) Engineering psychology and human performance. Psychology Press, Portland

    Google Scholar 

  63. Wiener EL (1981) Complacency: Is the term useful for air safety. In: Proceedings of the 26th corporate aviation safety seminar, 117, pp 116–125

  64. Wille JM, Saust F, Maurer M (2010) Stadtpilot: driving autonomously on Braunschweig’s inner ring road. Intelligent vehicles symposium IV 2010 IEEE, pp 506–511

  65. Wilson GF, Russell CA (2007) Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Hum Factors 49(6):1005–1018

    Google Scholar 

  66. Woods DD (1989) The effect of automation on the humans role: experience from non-aviation industries. Flight deck automation: promises and realities, pp 61–85, NASA CR-10036, 61–85

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Acknowledgements

We are indebted to Prof. Neil Millar from the University of Tsukuba and the anonymous referees for their insightful comments and feedback on this paper.

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Correspondence to Husam Muslim.

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Muslim, H., Itoh, M. A theoretical framework for designing human-centered automotive automation systems. Cogn Tech Work 21, 685–697 (2019). https://doi.org/10.1007/s10111-018-0509-8

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Keywords

  • Human-centered automation
  • Human–automation interactions
  • Authority
  • Levels of automation
  • Automation driving system
  • Adaptive automation
  • Shared control