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      Out-of-the-loop crash prediction: the automation expectation mismatch (AEM) algorithm


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          This study uses behavioural data from the complete drive for a subset of 54 participants from the automation expectation mismatch set of test track experiments and aims to develop an algorithm that can predict which drivers are likely to crash. Participants experienced 30 min of highly reliable supervised automation and were required to intervene to avoid crashing with a stationary object at the end of the drive. Many of them still crashed, despite having their eyes on the conflict object. They were informed about their role as supervisors, automation limitations, and received attention reminders if visually distracted. Three pre-conflict behavioural patterns were found to be associated with increased risk of crash involvement: low levels of visual attention to the forward path, high per cent road centre (i.e. gaze concentration), and long visual response times to attention reminders. One algorithm showed very high performance in classifying crashers when combining metrics related to all three behaviours. This algorithm is possible to implement as a real-time function in eye-tracker equipped vehicles. The algorithm can detect drivers that are not sufficiently engaged in the driving task, and provide feedback (e.g. reduce function performance, turn off function) to increase their engagement.

          Most cited references23

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          Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation.

          B Muir, N Moray (1996)
          Two experiments are reported which examined operators' trust in and use of the automation in a simulated supervisory process control task. Tests of the integrated model of human trust in machines proposed by Muir (1994) showed that models of interpersonal trust capture some important aspects of the nature and dynamics of human-machine trust. Results showed that operators' subjective ratings of trust in the automation were based mainly upon their perception of its competence. Trust was significantly reduced by any sign of incompetence in the automation, even one which had no effect on overall system performance. Operators' trust changed very little with experience, with a few notable exceptions. Distrust in one function of an automatic component spread to reduce trust in another function of the same component, but did not generalize to another independent automatic component in the same system, or to other systems. There was high positive correlation between operators' trust in and use of the automation; operators used automation they trusted and rejected automation they distrusted, preferring to do the control task manually. There was an inverse relationship between trust and monitoring of the automation. These results suggest that operators' subjective ratings of trust and the properties of the automation which determine their trust, can be used to predict and optimize the dynamic allocation of functions in automated systems.
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            Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs

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              Sensitivity of eye-movement measures to in-vehicle task difficulty


                Author and article information

                IET Intelligent Transport Systems
                IET Intell. Transp. Syst.
                The Institution of Engineering and Technology
                24 April 2019
                22 May 2019
                August 2019
                : 13
                : 8
                : 1231-1240
                Volvo Cars Safety Centre, Volvo Cars , Gothenburg, Sweden
                Author information
                IET-ITS.2018.5555 ITS.2018.5555.R1

                This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License ( http://creativecommons.org/licenses/by-nc/3.0/)

                : 15 November 2018
                : 29 March 2019
                : 15 April 2019
                Page count
                Pages: 0
                Funded by: VINNOVA
                Award ID: 2014-06012
                Special Section: Selected papers from the 6th International Conference on Driver Distraction and Inattention (DDI2018)

                General engineering,Electrical engineering
                road traffic,out-of-the-loop crash prediction,automation expectation mismatch algorithm,behavioural sciences computing,visual perception,road safety,preconflict behavioural patterns,high performance,road vehicles,crash involvement,driver information systems,gaze concentration,visual response times,driving task,AEM algorithm,behavioural data,supervised automation,attention reminders,visual attention,stationary object,conflict object


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