2019
DOI: 10.1177/0018720819875347
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How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

Abstract: Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent … Show more

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Cited by 26 publications
(19 citation statements)
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References 50 publications
(100 reference statements)
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“…The present findings demonstrate that this pattern holds for silent failures on bending roads, across a wide range of failure criticalities. The non-unity increase could have implications for the perceptual mechanisms underpinning how drivers decide when to intervene in silent failures [7]. The perceptual error at response (quantified by lower TLC T values) decreased with more gradual failures.…”
Section: Plos Onementioning
confidence: 99%
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“…The present findings demonstrate that this pattern holds for silent failures on bending roads, across a wide range of failure criticalities. The non-unity increase could have implications for the perceptual mechanisms underpinning how drivers decide when to intervene in silent failures [7]. The perceptual error at response (quantified by lower TLC T values) decreased with more gradual failures.…”
Section: Plos Onementioning
confidence: 99%
“…Such behaviour could be explained by accounts of drivers responding to the accumulated perceptual error, equating integration of a small error over a long time with the integration of a large error over a short time, resulting in responses at smaller absolute error in less urgent situations (cf. [7,9,51]). Though TLC T increased for less critical failures, TLC T values decreased due to slower responses when drivers were engaged with the auditory cognitive task.…”
Section: Plos Onementioning
confidence: 99%
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“…We and others have investigated the application of drift diffusion-type models in the road traffic context, with promising results initially for low-level locomotion decisions on applying braking or steering control [39,48,71], more recently also extending to multi-agent interaction situations [2,24,31,41,73].…”
Section: Introductionmentioning
confidence: 99%
“…This feature allows the driver to specify its maximum speed and minimum distance to the preceding vehicle. Examples of this work can be seen in [104] where driving simulation is used to explore the use of an ACC system in traffic situations, and in [105] where system failure is explored in different traffic scenarios.…”
Section: B Autonomous Systems Developmentmentioning
confidence: 99%