Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk (2014) / Chapter Skim
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1Background 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.
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2incidents together. Detailed driving behavior data recorded in the seconds leading up to crashes and near crashes cannot be obtained from test tracks, simulators, or observational data (e.g., crash databases)
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3 designed, manufactured, and installed in each volunteer's own vehicle. Data were recorded continuously while the participant's vehicle was operating and sampled at the original resolution of the sensors.
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4Key Results Risk from Distracting Activities (Secondary Tasks) The analysis started by replicating previous findings.
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5 Figure ES.1 shows the ORs associated with specific distracting activities. The precise OR is shown in the center of each dot, and the lines surrounding the dots indicate the 95th percentile confidence interval.
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6Most Sensitive Glance Risk Metrics To determine whether risk from distracting activities (secondary tasks) can be explained by glance behavior, it was necessary to first find the most predictive glance metrics.
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7 Timing of Eyes off Path Relative to Situation Kinematics and Visual Cues In Figure ES.4, it can be observed that the crashes, near crashes, and matched baselines are relatively well separated in this state space (i.e., Glance Length and the rate of change of inverse Time to Collision)
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8change rate. Thus, an important finding of the present analysis is that glances that lead to crashes may not necessarily have to be long.
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9 Discussion and Conclusions What are the most dangerous glances away from the road, and what are safer glances? The team's initial answer to this question was that the most dangerous and safest glances are quantified by a three-metric glance model.
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10 crashes and minimum time to collision (minTTC) for near crashes -- are still the most relevant metrics when analyzing actual severity (what actually happened in the event)
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11 the safety margins needed to protect the driver if the situation changes rapidly during an off-path glance by creating more time headway, issuing warnings to alert the driver to rapid closure rates, and actively braking. For education and behavioral change, it is recommended that the public be made aware of the inopportune glance mismatch mechanism, that the importance of adopting safe headways be emphasized (particularly for ages 16–17 and 76+)
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