SWOV Catalogus

339979

Analysis of naturalistic driving study data : safer glances, driver inattention, and crash risk. SHRP 2 Safety Project S08A, prepublication draft, not edited.
20141219 ST [electronic version only]
Victor, T. Bargman, J. Boda, C.-N. Dozza, M. Engstrom, J. Flannagan, C. Lee, J.D. & Markkula, G.
Washington, D.C., Transportation Research Board TRB, 2014, 186 p., ref.; The Second Strategic Highway Research Program SHRP 2 ; SHRP 2 Safety Project S08A

Samenvatting Communication technology pervades our daily living, and is increasingly integrated into the car, where it has the potential to distract drivers. Consequently, there is a critical need to better understand distraction and the limits of attention while driving. Distracted driving, which has long been a contributor to motor vehicle crashes, is flourishing in the fertile environment of communication, information, and entertainment technology that is transforming the car. Distraction includes instances where drivers take their eyes off the road-visual distraction-and instances where drivers take their mind off the road cognitive distraction. According to the US-EU Driver distraction and HMI Working Group, driver inattention is defined as a mismatch between the current attention allocation (distribution) and that demanded by activities critical for safe driving, whereas driver distraction is defined as diversion of attention away from activities critical for safe driving to one or more activities that are not critical for safe driving. Driver inattention is thus conceived of in terms of mismatches between the current allocation of attention and that demanded by activities critical for safe driving. In the current context the activity critical for safe driving is attention to- and control of headway to the lead vehicle. The specific mechanisms and indicators of the risk of inattention are unfortunately not definitively quantified. Initial analyses of the 100-car study focused on general relationships, such as the proportion of crashes involving inattention as a contributing factor, or the relative and population-attributable risk associated with different inattention-related activities. Subsequent analyses have examined the influences of various characteristics such as total eyes-off-road time (glance history), single glance duration, and glance location. Previous work has focused on calculating the risk associated with (human identified) classifications of distracting tasks, such as talking, dialling, eating, texting, etc. Although the task risk approach has merit, especially for policy decisions and education on what tasks should or shouldn't be done while driving, it does not tell us why the tasks are dangerous nor do they provide the inattention performance risk information needed for many countermeasures. It is important to be able to determine if the particular way a driver is doing a task (e.g. radio tuning) is dangerous than to be able to detect what task is being done. The radio can be tuned in a safe or unsafe way and the inattention performance quantification approach presented here focuses on being able to measure this and, in various ways, provide countermeasures based on this. Naturalistic Driving Data is valuable in comparison to driving simulator and field experiments because it is able to quantify real crash risk but naturalistic driving data up until now with the SHRP 2 data set has had a limited number of crashes. Risk has generally been calculated for Safety-Critical-Events which group crashes, near crashes, and incidents together. Detailed driving behaviour data recorded in the seconds leading up to crashes and near-crashes is not possible to obtain from test tracks, simulators, or observational data (e.g., crash databases). The SHRP 2 naturalistic driving study can provide the data that is needed to provide inattention performance measures associated with pre-crash situations. The data is essential to improve the understanding of driver inattention, for guidelines to reduce distraction from electronics devices, for countermeasures that detect and act to reduce distraction while driving, and for regulation and education. The current research aims to determine the relationship between driver inattention and crash risk in lead-vehicle pre-crash scenarios (corresponding to rear-end crashes). It aims to develop inattention-risk relationships describing how an increase in inattention performance variables combines with context in lead-vehicle pre-crash scenarios to increase risk. The inattention-risk relationships are intended to show which glance behaviours are safer than others and pinpoint the most dangerous glances away from the road. A glance is the time from the moment at which the eyes move towards an area of interest (such as the radio, rear-view mirror, or forward path) to the moment they move away from it. The results aim to (1) support distraction policy, regulation, guidelines, (2) improve intelligent vehicle safety systems, and (3) teach safe glance behaviours. (Author/publisher)
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