Influence of human-machine interactions and task demand on automation selection and use

Abstract A seminal work by Sheridan and Verplank depicted 10 levels of automation, ranging from no automation to an automation that acts completely autonomously without human support. These levels of automation were later complemented with a four-stage model of human information processing. Next, human-machine cooperation centred models and associated cooperation modes were introduced. The objective of the experiment was to test which human-machine theorie describe automation use better. The participants were asked to choose repeatedly between four automation types (i.e. no automation, warning, co-action, function delegation) to complete three multi-attribute task battery tasks. The results showed that the participants favour the selection of automation types offering the best human-machine interactions quality rather that the most effective automation type. Contrary to human-machine cooperation models, technology centred models could not predict accurately automation selection. The most advanced automation was not the most selected. Practitioner Summary: The experiment dealt with how people select different automation types to complete the multi-attribute task battery that emulates recreational aircraft pilot tasks. Automation performance was not the main criteria that explain automation use, as people tend to select an automation type based on the quality of the human-machine cooperation.

[1]  Iyad Rahwan,et al.  The social dilemma of autonomous vehicles , 2015, Science.

[2]  Iris Davis,et al.  An Updated Version of the U.S. Air Force Multi-Attribute Task Battery (AF-MATB) , 2014 .

[3]  Alexandra Fort,et al.  Automotive HMI design and participatory user involvement: review and perspectives , 2017, Ergonomics.

[4]  Thomas B. Sheridan,et al.  Human and Computer Control of Undersea Teleoperators , 1978 .

[5]  Klaus Bengler,et al.  Requirements for cooperative vehicle guidance , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[6]  J. R. Comstock.  MAT - MULTI-ATTRIBUTE TASK BATTERY FOR HUMAN OPERATOR WORKLOAD AND STRATEGIC BEHAVIOR RESEARCH , 1994 .

[7]  R Parasuraman,et al.  Designing automation for human use: empirical studies and quantitative models , 2000, Ergonomics.

[8]  Edward M. Hitchcock,et al.  Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. , 2013, Journal of experimental psychology. Applied.

[9]  Jordan Navarro,et al.  Lateral control assistance in car driving: classification, review and future prospects , 2011 .

[10]  Christopher D. Wickens,et al.  Automation Reliability in Unmanned Aerial Vehicle Control: A Reliance-Compliance Model of Automation Dependence in High Workload , 2006, Hum. Factors.

[11]  Gerald J.S. Wilde,et al.  Risk homeostasis theory and traffic accidents: propositions, deductions and discussion of dissension in recent reactions , 1988 .

[12]  Nadine Matton,et al.  A neuroergonomics perspective on mental workload predictions in Jens Rasmussen’s SRK framework , 2017 .

[13]  P A Hancock,et al.  Automation: how much is too much? , 2014, Ergonomics.

[14]  Erik Hollnagel,et al.  Cognitive Systems Engineering: New Wine in New Bottles , 1983, Int. J. Man Mach. Stud..

[15]  Zhizhong Li,et al.  Task complexity: A review and conceptualization framework , 2012 .

[16]  S. G. Hart,et al.  Development of NASA-TLX(Task Load Index) , 1988 .

[17]  Victoria A Banks,et al.  Keep the driver in control: Automating automobiles of the future. , 2016, Applied ergonomics.

[18]  François Osiurak,et al.  When Do We Use Automatic Tools Rather Than Doing a Task Manually? Influence of Automatic Tool Speed. , 2015, The American journal of psychology.

[19]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[21]  G J Wilde,et al.  Risk homeostasis theory: an overview , 1998, Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention.

[22]  Mark Mulder,et al.  Sharing Control With Haptics , 2012, Hum. Factors.

[23]  Huiyang Li,et al.  Human Performance Consequences of Stages and Levels of Automation , 2014, Hum. Factors.

[24]  Frank Flemisch,et al.  Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations , 2012, Cognition, Technology & Work.

[25]  A W Stedmon,et al.  Semi-automated CCTV surveillance: the effects of system confidence, system accuracy and task complexity on operator vigilance, reliance and workload. , 2013, Applied ergonomics.

[26]  Peter A. Hancock.  Mind, Machine and Morality: Toward a Philosophy of Human-Technology Symbiosis , 2017 .

[27]  Jean-Michel Hoc,et al.  Towards a cognitive approach to human-machine cooperation in dynamic situations , 2001, Int. J. Hum. Comput. Stud..

[28]  Annika F.L. Larsson,et al.  Learning from experience: familiarity with ACC and responding to a cut-in situation in automated driving , 2014 .

[29]  Christopher D. Wickens,et al.  Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human–Automation Interaction , 2017, Hum. Factors.

[30]  Peter A Hancock,et al.  State of science: mental workload in ergonomics , 2015, Ergonomics.

[31]  Christophe Jallais,et al.  The impact of false warnings on partial and full lane departure warnings effectiveness and acceptance in car driving , 2016, Ergonomics.

[32]  Jordan Navarro,et al.  Obstacle avoidance under automated steering: Impact on driving and gaze behaviours , 2016 .

[33]  Guy H. Walker,et al.  AUTOMATING THE DRIVER'S CONTROL TASKS , 2001 .

[34]  Jordan Navarro,et al.  Human–machine interaction theories and lane departure warnings , 2017 .

[35]  François Osiurak,et al.  To do it or to let an automatic tool do it? The priority of control over effort. , 2013, Experimental psychology.

[36]  Alexandra Fort,et al.  Digital, analogue, or redundant speedometers for truck driving: Impact on visual distraction, efficiency and usability. , 2017, Applied ergonomics.

[37]  Mark S. Young,et al.  Cooperation between drivers and automation: implications for safety , 2009 .

[38]  Paul Nightingale,et al.  الهدم الخلاق Creative Destruction , 2014 .

[39]  Thomas K. Ferris,et al.  Cockpit Automation: Still Struggling to Catch Up… , 2010 .

[40]  N. Stanton,et al.  Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems , 2016, Ergonomics.

[41]  Natasha Merat,et al.  How do Drivers Behave in a Highly Automated Car , 2017 .

[42]  R. Wood.  Task complexity: Definition of the construct , 1986 .

[43]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[44]  Yamira Santiago-Espada,et al.  The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide , 2011 .

[45]  Glenn F. Wilson,et al.  Human-Automation Interaction Research , 2013 .

[46]  Juergen Sauer,et al.  How operators make use of wide-choice adaptable automation: observations from a series of experimental studies , 2018 .

[47]  J Navarro,et al.  Influence of lane departure warnings onset and reliability on car drivers' behaviors. , 2017, Applied ergonomics.

[48]  Christopher D. Wickens,et al.  The benefits of imperfect diagnostic automation: a synthesis of the literature , 2007 .

[49]  Timothy D. Wilson,et al.  Just think: The challenges of the disengaged mind , 2014, Science.

[50]  Linda Onnasch,et al.  Human Performance Consequences of Automated Decision Aids , 2012 .