In this study, researchers Liang (Liberty Mutual Institute for Safety), Lee (U of Wisconsin) and Yekhshatyan (U of Iowa) compared 24 algorithms intended to estimate driver distraction and predict crash and near-crash risk as it relates to driver glance behavior.
They provide the following example statistics ion their introduction:
The researchers postulate that algorithms that quantify drivers’ eye-glance patterns in terms of glance duration, glance history, and glance location, could be a useful way to assess risk related to visual driver distraction, as well as some cognitive distractions, since the two are often intertwined (e.g., glancing at a keypad while texting). However, some cognitive distractions, such as talking on a hands-free, do not involve eye-glance distractions.
Other researchers have already investigated and found evidence for these visual distractions.
Liang et al. conducted their analysis using the "100 Car Naturalistic Driving Study," a data set collected from over 241 drivers who were unobtrusively monitored over approximately 2 million miles of actual driving conditions. This data set was collected by other researchers, and has been used for a variety of driver behavior studies. For this study, they relied on two of the 100-Car databases:
They only included events that contained appropriate eye-tracking data and in which the driver was determined to be at fault, resulting in a total of 359. Building on the work of other researchers, the researchers then tested a variety of algorithms for their ability to predict increased crash/near-crash risk as defined by an increased odds-ratio. They present a great deal of detail on their methodology in the full article, referenced below, and interested readers are encouraged to read it for a complete understanding of their methods.
Their key findings include:
What this Might Mean for Ergonomists
I have to admit, when I first saw this study I was very hopeful it would contain more detailed and precise findings that we could begin to readily apply. However, as I dissected the entire article it became apparent that it left more questions unanswered than answered. Essentially, they found that glancing away from the road does indeed increase crash/near-crash risk, which is no surprise. They also found that the 100-Car data set, which predates the influx of mobile phones and texting devices, does not have the level of detailed eye-tracking data needed to achieve more precise predictive algorithms. So, why did they even rely on such "old" and "incomplete" data in the first place? Presumably because there is no better data available for real drivers in real driving conditions that involved crashes and near-crashes, and by spending the time to analyze this existing data set, they can now make a strong claim that "additional research is needed," the siren song of all research studies.
The biggest take-away from this study, then, is that we still don't know enough to really understand the underlying mechanisms of driver distraction as they relate to eye-glancing behavior and the likelihood of a crash. Previous studies have tied glance duration, glance history and glance location to increased distraction and risk, but the precise relationships between these variables remains unclear.
Does that mean we're all safe to start phoning, texting, etc. while we drive? Of course not, but it does mean that if we're going to come up with design solutions that address the root-causes of driver distraction related crashes, which is the ultimate goal of ergonomics in this case, we're going to need better data. Sigh. The pace of research is slow indeed.
Yulan Liang, John D. Lee, and Lora Yekhshatyan, (2012). How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving. Human Factors, Online First, DOI:10.1177/0018720812446965, avaialble to Human Factors subscribers at the time of this wrighting at http://hfs.sagepub.com/content/early/2012/06/15/0018720812446965.full.pdf+html.
This article originally appeared in The Ergonomics Report™ on 2012-06-27.