01628203

Component

https://doi.org/10.3141/2663-11

Multitasking related demands can adversely affect drivers’ allocation of attention to the roadway, resulting in delays or missed responses to roadway threats and to decrements in driving performance. Robust methods for obtaining evidence and data about demands on and decrements in the allocation of driver attention are needed as input for design, training, and policy. The detection response task (DRT) is a commonly used method (ISO 17488) for measuring the attentional effects of cognitive load. The AttenD algorithm is a method intended to measure driver distraction through real-time glance analysis, in which individual glances are converted into a scalar value using simple rules considering glance duration, frequency, and location. A relationship between the two tools is explored. A previous multitasking driving simulation study, which used the remote form of the DRT to differentiate the demands of a primary visual–manual human–machine interface from alternative primary auditory–vocal multimodal human–machine interfaces, was reanalyzed using AttenD, and the two analyses compared. Results support an association between DRT performance and AttenD algorithm output. Summary statistics produced from AttenD profiles differentiate between the demands of the human–machine interfaces considered with more power than analyses of DRT response time and miss rate. Among discussed implications is the possibility that AttenD taps some of the same attentional effects as the DRT. Future research paths, strategies for analyses of past and future data sets, and possible application for driver state detection are also discussed.

01653375

17-06664

English

pp 82–89

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309442060

Digital/other

Figures (5) ; Photos; References (34) ; Tables (2)

Attention lapses; Behavior; Driver performance; Driver vehicle interfaces; Driving; Mathematical models; Multitasking; Real time information; User interfaces (Computer science)

Highways; Safety and Human Factors

TRIS, TRB, ATRI

Dec 8 2016 12:43PM

• Age-Based Simulation of Merging Behavior at Freeway Merging Ramps
• Analyzing and Modeling Drivers’ Deceleration Behavior from Normal Driving
• Automatic Driver Head State Estimation in Challenging Naturalistic Driving Videos
• Comparative Analysis of Toll Plaza Safety Features in Puerto Rico and Massachusetts with a Driving Simulator
• Crash Heterogeneity: Implications for Naturalistic Driving Studies and for Understanding Crash Risks
• Drivers’ Lane-Keeping Ability in Heavy Rain: Preliminary Investigation Using SHRP 2 Naturalistic Driving Study Data
• Effects of a Change in Environment on the Minimum Time to Situation Awareness in Transfer of Control Scenarios
• Effects of Alert Cue Specificity on Situation Awareness in Transfer of Control in Level 3 Automation
• Examining Potential Confounds in Use of the Detection Response Task for In-Vehicle System Evaluation
• How to Drive Drivers Nuts: Effect of Auditory, Vibrotactile, and Multimodal Warnings on Perceived Urgency, Annoyance, and Acceptability
• Investigating Driver Behavior at Minor-Street Stop-Controlled Intersections in Qatar
• Modeling Effects of Forward Glance Durations on Latent Hazard Detection
• Modeling of Vehicles Merging Movement at Unsignalized Intersections Considering Drivers’ Risk Perception
• On-Road Visual Sign Salience, Driver Attention Allocation, and Target Detection Accuracy
• Simulator Study of Involuntary Driver Distraction Under Different Perceptual Loads
• Strategies to Assist Drivers in Remaining Attentive While Under Partially Automated Driving: Verification of Human–Machine Interface Concepts