Driver Workload Metrics Task 2 Final Report

Angell, Linda; Auflick, Jack L.; Austria, P A; Kochhar, Dev S.; Tijerina, Louis; Biever, Wayne; Diptiman, Tuhin; Hogsett, J; Kiger, S

doi:10.1037/e729342011-001

Cited by 140 publications

(120 citation statements)

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“…Therefore, to approximate the ideal case and retain the best possible chance of getting reliable and accurate eyegaze estimates, the data were manually reduced. The procedure followed was similar to those followed in the National Highway Traffic Safety Administration (NHTSA) lane-change and workload experiments [3], [4] to produce output that a real-world eye-gaze tracker would output in an optimal setting.…”

Section: ) Head Dynamicsmentioning

confidence: 99%

“…Simulators though do not capture all the dynamics and variability of real-world environments [4]. Some real-world studies of driver behavior during lane changes have measured eye gaze by manually reducing data [3], [4], [15], [23], [24].…”

Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

“…Nine different gaze locations were derived from the procedure described in [4] as relevant to the task at hand: Looking Straight, Glancing Left or Right, Looking at Dash or Rearview Mirror, Looking at Left or Right Mirrors, and Looking Over Left or Right Shoulders. Sample images from each of these cases are shown in Fig.…”

Section: ) Head Dynamicsmentioning

confidence: 99%

See 2 more Smart Citations

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

246

124

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Abstract-Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system Index Terms-Driver-assistance systems, driver behavior, driver intent inference, intelligent vehicles, machine vision, sparse Bayesian learning.

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Section: ) Head Dynamicsmentioning

confidence: 99%

Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

See 1 more Smart Citation

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

246

124

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show abstract

“…high visual and dynamic fidelity) does not necessarily ensure high predictive validity (Harms et al, 1996;Curry et al, 2005). Likewise, a lab test with low face validity can have high predictive validity -the Sternberg lab test is apparently unrelated to event detection, but is a good predictor of event detection on the road (Angell et al, 2006). We investigate here the predictive validity of a low-cost, low-fidelity simulator for event detection on the road.…”

Section: Introductionmentioning

confidence: 99%

Validation of the Static Load Test for Event Detection During Hands-Free Conversation

Young

Angell

Sullivan

et al. 2009

Proceedings of the 5th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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Summary:Objective. To see if visual event reaction times (RTs) during handsfree conversation conditions in the Enhanced Static Load Test (ESLT) can predict RTs in similar conditions in on-road driving. Methods. Brake reaction times to random center and side light events were measured while watching a driving video, attempting to keep a marker in the center of the lane with a steering wheel, answering the phone by pressing a button, and carrying on neutral or angry handsfree conversations in covert (silent) or overt mode on a hands-free phone device. Open-road tests were conducted in traffic for subjects with similar side and front light events, with foot reaction times measured while engaged in the same secondary tasks and conditions. Results. Mean RTs for the task segments in the lab were predictive of the mean RTs for the corresponding task segments in the on-road test (r = 0.90, df = 16, p < 0.000001). Conclusion. This study validates the Enhanced Static Load Test as predictive of visual event RTs during open-road driving for the range of experimental conditions and tasks considered.

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“…Many experimental studies have investigated the effect of different types of stimulus-response tasks on driving performance (e.g., Angell et al, 2006;Shutko, Mayer, Laansoo, & Tijerina, 2009;Young, Hsieh, & Seaman, 2013;Reimer et al, 2014b). According to the literature, visual and auditory modalities are two of the most frequently used information presentation channels in the in-vehicle secondary tasks, whereas manual and speech (or verbal) input techniques are the most common responding methods.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Stimulus-Response Secondary Tasks with Different Modalities while Driving in a Computational Cognitive Architecture

Jeong

Liu

2017

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

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Summary: This paper introduces a computational human performance model based upon the queueing network cognitive architecture to predict driver's eye glances and workload for four stimulus-response secondary tasks (i.e., auditorymanual, auditory-speech, visual-manual, and visual-speech types) while driving. The model was evaluated with the empirical data from 24 subjects, and the percentage of eyes-off-road time and driver workload generated by the model were similar to the human subject data. Future studies aim to extend the types of voice announcements/commands to enable Human-Machine-Interface (HMI) evaluations with a wider range of usability test for in-vehicle infotainment system developments.

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Driver Workload Metrics Task 2 Final Report

Cited by 140 publications

References 0 publications

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Validation of the Static Load Test for Event Detection During Hands-Free Conversation

Modeling of Stimulus-Response Secondary Tasks with Different Modalities while Driving in a Computational Cognitive Architecture

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