Quantifying Driver Response Times Based upon Research and Real Life Data

Muttart, Jeffrey

doi:10.17077/drivingassessment.1137

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…The values started to decline immediately after lane change. Note that this 3 seconds prediction are perfectly enough for the majority of driving response times [4]. Similar results were obtained during left and right lane changes, turns and braking.…”

Section: Methodssupporting

confidence: 78%

Poster

Navuluri

Padia

Gupta

et al. 2011

Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services

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In recent years, the enforcement of safety features like seatbelt and reactive automotive technologies like lane assistance system etc. have enabled drivers to drive more safely and effectively. Better safety can be achieved by using proactive technologies to predict driver's intentions ahead of time and inform surrounding drivers of the course of the current action. This work introduces a novel system to predict driver's intention based on Electroencephalography (EEG).

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Section: Methodssupporting

confidence: 78%

Poster

Navuluri

Padia

Gupta

et al. 2011

Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services

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“…Similar dependencies between situation kinematics (the relative motion of involved road users, in terms of distances, speeds, etc.) and BRT have been reviewed by Muttart (2003Muttart ( , 2005 and have also been demonstrated in more recent test track and driving simulator studies (Jurecki & Stanczyk, 2009, 2014Engström, 2010;Ljung Aust et al, 2013). However, a detailed, large-scale analysis is still outstanding, especially for naturalistic (i.e.…”

Section: Introductionmentioning

confidence: 92%

“…The RT usually represents the time duration from the appearance of a potential hazard, such as a lead vehicle's brake lights activating, until the driver under study initiates some form of evasive response (Society of Automotive Engineers, 2015). Especially for braking responses, there is a considerable literature measuring brake reaction times (BRTs) and how they are influenced by factors such as driver age, gender, cognitive load, situation urgency, number of stimuli for the driver to consider, warnings, and so on (see for example the studies by Barrett et al, 1968;Olson and Sivak, 1986;Fambro et al, 1998;McGehee et al, 1999;Lee et al, 2002;Stanczyk, 2009, 2014;Fitch et al, 2010;Ljung Aust et al, 2013; and the reviews by Olson, 1989;Green, 2000;Muttart, 2003Muttart, , 2005). …”

Section: Introductionmentioning

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

128

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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“…Studies by Muttart [9] have quantitatively examined the driver response times based on various real conditions on the road. Their conclusion suggested that there is a variety in response time in drivers that is obviously correlated to driver speed and conditions.…”

Section: Introductionmentioning

confidence: 99%

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

Salehian

Far

2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Fatigue induced vehicle accidents have seen an increase in the last few decades. Fatigue monitoring using noninvasive and real time image processing and computer vision techniques have shown great promise and are an active research area. To that extent, in the proposed work a blink detection algorithm is proposed that serves as a visual cue that may be correlated to the state of fatigue of the driver. Using a complimentary but independent approach, shape analysis and histogram analysis are carried out in parallel to perform the blink detection task. Close to real time performance and a high level of accuracy in controlled settings show great promise of such approach in enhancing the monitoring of the driver's blinking patterns. One of the main constraints of using such algorithm in a real world setting is the minimized processing time required to allow for sufficient driver response time. In this work implementation of the algorithm is described using optimization techniques to meet such latency requirements. The validation of the algorithm was carried out by visual inspection of the video sequences in terms of precision and accuracy. The presented blink detection algorithm has a precision rate of 84% and an accuracy rate of 69% obtained through using 12 sequences of different duration videos in varying lighting conditions using a small sample of participants.

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Quantifying Driver Response Times Based upon Research and Real Life Data

Cited by 21 publications

References 5 publications

Poster

Poster

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

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

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