Acknowledgement: This work is partly funded by SAFER Vehicle and Traffic Safety Centre at 12Chalmers and Chalmers Area of Advanced Transport. The work was carried out within the 13 SAFER collaborative environment. We thank the reviewers for their constructive feedback. 14 15 Objective: To outline a conceptual framework for understanding driving style and, based on 19 this, review the state-of-the-art research on driving styles in relation to road safety. 20Background: Previous research has indicated a relationship between the driving styles 21
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Objective: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. Background: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. Method: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. Results: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. Conclusion: Effects of cognitive load on driving are strongly selective and task dependent. Application: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.
ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.Note: This is a freely distributable postprint, which does not reflect final copyediting and typesetting of the published article, to be cited as: Markkula, G., Engström, J., Lodin, J., Bärgman, J., Victor, T., 2016. A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accident Analysis & Prevention, xx(x), xxxx-xxxx. Abstract: Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5 s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined "hazard onset", but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario.
Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.
Despite an abundant use of the term "Out of the loop" (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions.
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