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Browsing by Author "Angell, Linda"

Now showing 1 - 5 of 5
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    Constructing a Distracted Driving Dataset
    Foley, James; Ebe, Kazu; Owens, Justin M.; Angell, Linda; Hankey, Jonathan M. (2014-08-25)
    Distracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community. Method This project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event. Results 1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset. Discussion We anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving. Practical applications The coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research.
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    An Exploration of Driver Behavior During Turns at Intersections (for Drivers in Different Age Groups)
    Angell, Linda; Aitch, Sudipto; Antin, Jonathan F.; Wotring, Brian (National Surface Transportation Safety Center for Excellence, 2015-06-23)
    A two-phase study of driver behavior at intersections was conducted using a naturalistic paradigm. In Phase 1, the behavior of teen, middle-aged, and older drivers was compared for left turns at three types of left turns across path (LTAP) intersections. Phase II was a follow-on effort focused more narrowly on a single T-intersection that included both left and right turns. A data-mining algorithm was used to aggregate data from two different naturalistic databases to obtain instances of unprotected turns at the same intersection and instances from a comparison protected turn at a signalized intersection. Several dependent variables were analyzed, including visual scanning measures, head-turning measures, speed approaching and driving through the intersection, and gap acceptance and rejection times. Results from Phase 1 show that driver behavior differed between the unprotected turns and the comparison protected turn. The two types of unprotected turns also had different effects on scan patterns and glance durations through the initiation, conflict, and completion zones of the turn. Definitive age-group effects were seen with head turning. The Phase 1 results suggest that older drivers scanned more narrowly and that they strategically oriented their scans in the direction of greatest threat for certain types of unprotected left turns. Results from Phase 2 replicated those from Phase 1. For left turns, most driver groups had a similar distribution of glances by location for this T-intersection, with the forward-looking glances constituting almost half of all glances, followed by glances through the window areas. However, older drivers showed a higher proportion of glances to the right, indicating a strategic shift of attention that was consistent with the head-turn analysis in Phase 1. Link analyses done on the visual scan patterns of drivers indicated that older drivers and middle-aged drivers had more-organized and more-strategic visual scans of the intersection compared with young drivers. While middle-aged drivers and younger drivers scanned broadly, older drivers had a narrower scan that was oriented slightly toward the right for a left turn and to the left for a right turn, perhaps indicating attention to gap selection and threats in the stream of traffic into which they were merging. Visual entropy analyses showed that active visual scanning to a larger number of areas increased for all age groups when traffic was present. This increase was most pronounced for younger drivers. Significant differences were observed for peak speed and average speed during a left turn, with older drivers using lower speeds throughout intersections and completing their turns at lower speeds than other age groups. These findings suggest that the behavior of older drivers is at least partly strategic in nature. The changes in scanning exhibited by older drivers appear to be more focused “versions” of the visual scanning typical of middle-aged drivers, tuned specifically to areas of highest threat during turns. However, this may lead to some neglect of other areas, and it is possible that age-related changes in perception and cognition may be contributing to some of the differences.
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    Identification of Cognitive Load in Naturalistic Driving
    Angell, Linda; Perez, Miguel A.; Soccolich, Susan A. (National Surface Transportation Safety Center for Excellence, 2015-07-28)
    Over the last decade, naturalistic driving studies have provided significant insight into issues pertaining to the roles of distraction, inattention, and drowsiness in crash risk. New techniques have been developed for coding naturalistic data, mining it, and analyzing it. The application of these techniques has begun to deepen our understanding of how different types of secondary tasks may vary in their effects on crash risk. However, one methodological gap that still remains in the field concerns techniques for identifying periods of cognitive load within streams of naturalistic driving data. Cognitive load is a critical element of current concerns about driver distraction, particularly with regards to intentional tasks of listening and conversing while driving, but also as related to spontaneously occurring processes like daydreaming and becoming lost in thought, which may also take place while driving. The research reported here was undertaken to develop a methodology for identifying epochs of cognitive activity occurring while driving by using indicators of cognitive load that are based on eye behavior. It furthermore set out to test the ability of these metrics, when used together as part of a model or algorithm, to discriminate pre-identified epochs of cognitive task load from “just driving” baselines drawn from naturalistic driving data. To accomplish these goals, the project mined an existing naturalistic driving database to extract the following types of driving epochs: those containing conversation, which was operationally defined as involving cognitive load; those containing visual-manual interaction; and those containing just driving. Data from these epochs were augmented with new measures related to three hypothesized cognitive load indicators. The measures which were explored as behavioral markers of cognitive load included the following and were derived from the acquired naturalistic data: (a) very long glances to the forward road, coupled with (b) increased concentration of glances to the forward road, (c) reduced breadth of active scanning, and (d) changes in blink rate relative to other types of task loads as well as just driving baselines. Results of mixed-model analyses confirmed that cognitive epochs corresponded with the hypothesized patterns on two of the four indicators. However, the analysis revealed a surprising finding on all metrics: the “just driving” baseline epochs behaved as if they, too, contained significant amounts of cognitive load, perhaps in the form of daydreaming or mind-wandering. This finding complicated the modeling effort, which, although completed, yielded inconclusive results. The logistic regression approach showed much promise as a technique, but the predictor variables in the final model were difficult to interpret, and again seemed consistent with the notion that the just driving baselines, which were intended to serve as comparison epochs, were instead similarly composed of cognitive load periods. These latter epochs’ cognitive loads were simply of a different type—namely, daydreaming, thinking, or mind-wandering activity. This finding, while unexpected, has several important implications, both for future efforts toward the development of algorithms used to identify types of task load from naturalistic data, and for the use of baselines in epidemiological risk estimation.
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    In-vehicle device acquisition and usage in personal vehicles : commercial versus non-commercial driver's license holders
    Wotring, Brian; Angell, Linda; Antin, Jonathan F. (National Surface Transportation Safety Center for Excellence, 2011-03-28)
    A survey was administered to 1,524 Virginia Tech faculty, staff, and students and Blacksburg Transit drivers in an effort to differentiate between commercial driver's license holders (72 of the respondents) and "regular" (i.e., non-commercial) drivers in terms of their ownership and personal in-vehicle usage of handheld devices (e.g., cell phones and MP3 players). Results indicated that discrepancies exist between these two groups for some devices and usage. For instance, almost 35% of commercial drivers reported that they "never" text while driving their personal vehicles compared with only 4% of the non-commercial drivers.
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    Radio Tuning Effects on Visual and Driving Performance Measures: Simulator and Test Track Studies
    Perez, Miguel A.; Owens, Justin M.; Viita, Derek; Angell, Linda; Ranney, Thomas A.; Baldwin, G. H. Scott; Parmer, E.; Martin, John; Garrott, W. Riley; Mazzae, Elizabeth N. (United States. National Highway Traffic Safety Administration, 2013-05)
    Existing driver distraction guidelines for visual-manual device interface operation specify traditional manual radio tuning as a reference task. This project evaluated the radio tuning reference task through two activities. The first activity consisted of a static evaluation of the features and layouts of 12 original equipment vehicle radios. The second activity consisted of an experiment in which naÏve participants drove five models of vehicles on a test track while performing manual radio tuning tasks. Driving performance measures and eye glance behavior were examined during radio tuning and baseline (no secondary task) periods. Results showed differences between task and baseline periods in most measures as a function of radio design. Results of the test track radio running experiment were evaluated along with experimental data for radio tuning obtained in a driving simulator by NHTSA. Similar results were found for most eye glance measures. The data suggest the following visual demand acceptability criteria based upon driver 85th percentile radio tuning performance: - Individual eye glances away from the forward road scene should not exceed 1.3 seconds, and - Total eyes-off-road time to perform an entire task should not exceed 12.1 seconds. For compatibility with occlusion testing, these time values should be rounded off to multiples of 2.0 seconds. This gives task acceptability criteria of individual eye glances away from the forward road scene not exceeding 2.0 seconds and total eyes-off-road time to perform an entire task not exceeding 12.0 seconds.