Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

Victor, Trent; Dozza, Marco; Bärgman, Jonas; Boda, Christian-Nils Åkerberg; Engström, Johan; Markkula, Gustav

doi:10.17226/22297

Cited by 156 publications

(152 citation statements)

References 27 publications

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“…Within SHRP 2, the world's largest naturalistic driving study to date was carried out, collecting over 80 million kilometers of driving data from instrumented cars driven by 3147 drivers across six sites in the US. As noted above, the present paper describes analyses building on those by Victor et al (2015), comprising 46 crashes and 211 near-crashes; more specifically all of the critical events in the SHRP 2 database that were categorized as being of rear-end type (Scenarios 22-26 in the taxonomy by Najm and Smith, 2007) at the time of data extraction (spring of 2014).…”

Section: Data Setsmentioning

confidence: 99%

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Markkula

Engström

Lodin

et al. 2016

Accident Analysis & Prevention

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127

106

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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.

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Section: Data Setsmentioning

confidence: 99%

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Markkula

Engström

Lodin

et al. 2016

Accident Analysis & Prevention

Self Cite

127

106

View full text Add to dashboard Cite

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“…Subsequent work may wish to consider model performance across a larger and perhaps more spatially diverse set of glance objects with data drawn from a larger population of drivers. Further, efforts should assess the predictive power of head rotation data for certain types of glances such as those that are of longer duration and linked to greater risk of collision (Victor, 2014). Overall, this work suggests that head rotation data, a feature that can be recorded with commercially available sensors, may provide a potentially lower cost and reasonable estimate of attention allocation compared to eye tracking data.…”

Section: Discussionmentioning

confidence: 93%

Analysis of Drivers' Head and Eye Movement Correspondence: Predicting Drivers' Glance Location Using Head Rotation Data

Muñoz

Lee

Reimer

et al. 2015

Proceedings of the 8th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: Dri

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Summary:The relationship between a driver's glance pattern and corresponding head rotation is not clearly defined. Head rotation and eye glance data drawn from a study conducted by the Virginia Tech Transportation Institute in support of methods development for the Strategic Highway Research Program (SHRP 2) naturalistic driving study were assessed. The data were utilized as input to classifiers that predicted glance allocation to the road and the center stack. A predictive accuracy of 83% was achieved with Hidden Markov Models. Results suggest that although there are individual differences in head-eye correspondence while driving, head-rotation data may be a useful predictor of glance location. Future work needs to investigate the correspondence across a wider range of individuals, traffic conditions, secondary tasks, and areas of interest.

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“…While risk reduction evaluations might logically emphasize relatively well-established objective risk characteristics such as total task time (Burns, Harbluk, Foley & Angell, 2010) and glance metrics (Fitch et al, 2013;Klauer, Dingus, Neale, Sudweeks & Ramsey, 2006;Victor et al, 2014), it is likely that user perception continues to be an important consideration in the comprehensive assessment of user interfaces. Error rates or other factors, such as ease of initial engagement (Harvey, Stanton, Pickering, McDonald, & Zheng, 2011), may impact actual use of one design over another and should ideally be taken into account.…”

Section: Discussionmentioning

confidence: 99%

Considering Self-Report in the Interpretation of Objective Performance Data in the Comparison of HMI Systems

Mehler

Lee

Kidd

et al. 2017

Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: Dri

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Summary: Driver interaction with two production voice-command interfaces representing differing user interface design approaches were compared under onroad highway driving conditions. A sample of 80 drivers was randomly assigned to drive each vehicle (40 per vehicle). During voice-based phone contact calling and destination address entry, participants in one vehicle showed, on average, statistically significant "better" performance in terms of task completion time, mean glance duration, total off-road glance time, and total number of glances. However, these objective measures do not fully characterize the overall experience of participants. An analysis of error rates and subjective report of attitudes, effects on driving behavior, and behavioral intentions relative to their exposure to the two systems provided important, complementary and sometimes contrasting data about the relative advantages of each implementation.

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Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

Cited by 156 publications

References 27 publications

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Analysis of Drivers' Head and Eye Movement Correspondence: Predicting Drivers' Glance Location Using Head Rotation Data

Considering Self-Report in the Interpretation of Objective Performance Data in the Comparison of HMI Systems

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