Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles

Eriksson, Alexander; Stanton, Neville A.

doi:10.1177/0018720817728774

Cited by 68 publications

(47 citation statements)

References 39 publications

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“…Eriksson and Stanton [53] previously used the so-called COntextual COntrol model (COCOM) [54] to explain driver performance in a take-over scenario. This model states that successful tactical decision can be invoked by giving operators more time or by enhancing the predictability of the situation.…”

Section: E Aim Of This Experimentsmentioning

confidence: 99%

“…This model states that successful tactical decision can be invoked by giving operators more time or by enhancing the predictability of the situation. The authors used this to compare driver-paced transitions [53] (which allow for extra planning time) with transitions under time pressure (cf., [23] and [55]- [58]). In accordance with the predictions of the COCOM, we expected that improvements would occur in driver decision making by increasing the predictability of the situation through HMIs that involve different stages of automation.…”

Section: E Aim Of This Experimentsmentioning

confidence: 99%

“…6). The level of significance was visualized as the negative base-10 logarithm of the p-value, where large values represent small p-values in a similar fashion to the 'Manhattan' plot [53], [69]- [71]. Our use of multiple Wilcoxon signed-rank tests allows for a high temporal resolution (as opposed to using larger bin sizes and fewer tests).…”

Section: G Statistical Analysesmentioning

confidence: 99%

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Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

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This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ["A model for types and levels of human interaction with automation," IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisitionlow), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

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Section: E Aim Of This Experimentsmentioning

confidence: 99%

Section: E Aim Of This Experimentsmentioning

confidence: 99%

Section: G Statistical Analysesmentioning

confidence: 99%

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Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

Self Cite

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“…The slow response by the driver to increased demand by the vehicle (as is the case with automation failure) is explained by the time it takes for the attentional resource pool to increase to the point where manual control can occur. Driver paced take-over of control of the vehicle by drivers has been found to be more successful than vehicle-paced (Eriksson and Stanton, 2018). Research is moving toward the sociotechnical approach to automation design, that seeks to develop both the technical and human aspects in a coevolutionary manner, rather than the traditional techno-centric approach (Banks et al, 2014;.…”

Section: Introduction To Driving Automation and Autonomymentioning

confidence: 99%

Thematic issue: driving automation and autonomy

Stanton

2019

Theoretical Issues in Ergonomics Science

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Automated driving has the potential to support drivers, freeing them up to do other things, such as work, rest or play. The problem is that in the present instantiation, automated driving requires the driver to perform a monitoring function and be ready to intervene if required. This is the worst of all worlds of automation. The monitoring task can be (if performed properly) more demanding than manual driving and the driver is not freed up to do other things. Worse still, is that the monitoring task cannot be sustained for long and, on occasions, led to a vehicle collisions because the driver cannot intervene in a timely manner. One of the first studies conducted over twenty years shows this to be the case and there really have not been any improvements since. This special issue reports on the latest development in vehicle automation and points to future directions that research should be directed. Relevance to human factors/Relevance to ergonomics theoryDriving Automation and Autonomy is already upon us and the problems that were predicted twenty years ago are beginning to appear. These problems include shortfalls in expected benefits, equipment unreliability, driver skill fade, and error-inducing equipment designs. In addition, the driver becomes both physically and mentally detached from the task of driving and may engage with other non-driving tasks. Ironically, if the driver does not engage with other tasks then can suffer from reduced attentional resources (making them less able to regain control from the vehicle in an emergency). If the driver does engage with other non-driving tasks then their attentional resource pool do not deplete to the same extent (offering a protective effect) but the distraction of the other task(s) can slow down the reclaim of vehicle control from automation. This is one of the main dilemmas with automated driving, which the papers in this special issue address to a greater or lesser extent.

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“…Nonetheless, there is limited information publicly available with respect to control transitions pertinent to vehicles of higher automation levels. Most relevant studies address human factors and ergonomics aspects of the transitions either with the use of driving simulators or based on real world experiments (Eriksson & Stanton, 2017;Gold, Damböck, Lorenz, & Bengler, 2013;Gold, Körber, Lechner, & Bengler, 2016;Lu, Coster, & de Winter, 2017;Merat, Jamson, Lai, Daly, & Carsten, 2014) . These studies set the ground for the development of a novel ToC model, which is introduced in this paper that can replicate driver behavior during a ToC and vehicle motion during a MRM.…”

Section: Introductionmentioning

confidence: 99%

From Automated to Manual - Modeling Control Transitions with SUMO

Lücken¹,

Mintsis²,

Porfyri³

et al.

EPiC Series in Computing

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Transitions of Control (ToC) play an important role in the simulative impact assessment of automated driving because they may represent major perturbations of smooth and safe traffic operation. The drivers' efforts to take back control from the automation are accompanied by a change of driving behavior and may lead to increased error rates, altered headways, safety critical situations, and, in the case of a failing takeover, even to minimum risk maneuvers. In this work we present modeling approaches for these processes, which have been introduced into SUMO recently in the framework of the TransAID project. Further, we discuss the results of an evaluation of some hierarchical traffic management (TM) procedures devised to ameliorate related disturbances in transition areas, i.e., zones of increased probability for the automation to request a ToC.

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Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles

Abstract: It was shown that self-paced transitions could reduce the risk of accidents near the edge of the operational design domain. Vehicle manufacturers must consider these benefits when designing contemporary systems.

Cited by 68 publications

References 39 publications

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Thematic issue: driving automation and autonomy

From Automated to Manual - Modeling Control Transitions with SUMO

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