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Page 12
Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2014. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. Washington, DC: The National Academies Press. doi: 10.17226/22297.
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Page 12
Page 13
Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2014. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. Washington, DC: The National Academies Press. doi: 10.17226/22297.
×
Page 13
Page 14
Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2014. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. Washington, DC: The National Academies Press. doi: 10.17226/22297.
×
Page 14
Page 15
Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2014. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. Washington, DC: The National Academies Press. doi: 10.17226/22297.
×
Page 15

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12 To illustrate the central topic of the present research, consider the following examples. A driver is following a lead vehicle at a constant headway with the intention of merging into a lane on the freeway. While she glances over her shoulder for an appropriate gap to merge into, the lead vehicle suddenly brakes. When she looks back at the lead vehicle, it is too late to brake in time and a rear-end crash occurs. Alternatively, a driver could be reading a text message on his cell phone when the lead vehicle suddenly brakes in stop-and-go traffic. In each scenario, the driver is looking away from the forward view. The first example is perhaps more interesting because it illustrates that the driving task itself, not only secondary tasks, can cause inattention. Although the argument could be made that a good driver is always aware that an emergency situation could occur at any time, it is very difficult, if not impossible, to remain vigilant and keep attention on all relevant sources of information while driving. In particular, the simultaneous occurrence of an unexpected event with eyes-diverted has been hypothesized to play a key role in the causation of crashes and near crashes and has been a central motivating factor to pursue the present research. Communication technology pervades our daily living and is increasingly integrated into the car, where it has the poten- tial 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 con- tributor to motor vehicle crashes, is flourishing in the fertile environment of communication, information, and entertain- ment technology that is transforming the car. Distraction includes instances when drivers take their eyes off the road— visual distraction—and instances when 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 (Engström, Monk et al. 2013). 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. One of the greatest traffic safety challenges of our time is to eliminate or moderate crashes that are caused by driver in attention. Driver inattention is a long-standing major factor related to morbidity and mortality in motor vehicle crashes (Evans 2004). It is also a renewed problem associated with modern technology-based distractions such as the cell phone (NHTSA 2010a). In 2009 distraction was involved in crashes, causing 5,474 deaths and leading to 448,000 traffic injuries across the United States (NHTSA 2010b). Inattention to forward roadway—because of secondary tasks engagement, driving-related inattention to the forward roadway, nonspe- cific eyeglances, and fatigue—was identified as the primary contributing factor in 78% of all crashes, 93% of rear-end crashes, and 65% of near crashes in the National Highway Traffic Safety Administration (NHTSA) 100-car study (Dingus et al. 2006). The first three categories involve looking away from the forward roadway, and the last category involves loss of forward roadway vision from eyelid closure. Two main developments have combined in the past few years to create an escalation in priority of the driver distrac- tion and inattention issue: (1) research has been showing a much clearer association between driver inattention and crash risk, and (2) there is a growing concern over the compatibility with driving of the ever-increasing functionality available through electronic devices (such as smartphones and intelli- gent vehicle systems). The safety problem at issue in both these developments centers on problems related to driver inattention. Driver inattention is very high on the political and scientific agenda, and the industry is moving fast to respond both to enable the use of electronic functionality in a safe C h a p t e r 1 Background

13 revealed that risk is pinpointed to the timing of off-road glances in relation to external events. Risk is primarily associ- ated with an inopportune single glance duration (Victor and Dozza 2011; Victor et al., forthcoming). The most sensitive measures for risk were those that quantified an overlap of the off-road glance with the precipitating event—a change in the state of environment or action that began the sequence lead- ing to the crash or near crash (e.g., a lead vehicle that begins braking). The longer the driver looks away from the road at the time of the precipitating event, the greater the risk of a crash or near crash. However, these analyses did not look at what happened closer to the crash or near crash; rather, they looked only at the time of the precipitating event, which was at the start of the sequence leading to the crash or near crash and could be many seconds before the crash. More work is needed with the larger SHRP 2 data set to examine the relative contributions of different glance characteristics in relation to context, and specifically to examine the detailed mechanisms in the period of time between the precipitating event and the crash or near crash. In comparison with driving simulator and field experiments, naturalistic driving data are valuable because they are able to quantify real crash risk (e.g., NHTSA 2013). Until now, with the SHRP 2 data set, naturalistic driving data—have included a limited number of crashes. Risk has generally been calculated for safety-critical events, which groups together crashes, near crashes, and incidents. 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). The SHRP 2 Naturalistic Driving Study can provide the data that are needed for inattention performance measures associated with precrash situations. The data are essential to improve the understanding of driver inattention, for guide- lines to reduce distraction from electronics devices, for counter- measures that detect and act to reduce distraction while driving, and for regulation and education. 1.1 Summary of project aims This S08A research targets two of the highest prioritized global research questions identified for SHRP 2 in the S02 Phase 1 report (Boyle et al. 2010): • How do dynamic driver characteristics (e.g., inattention, fatigue, workload), as observed through driver performance measures, influence crash likelihood? (SHRP 2–GRQ1) • How does driver distraction influence crash likelihood? (SHRP 2–GRQ3) This effort focuses primarily on “driver characteristics, behavior, and performance”—one of the four priority areas manner and to reduce driver inattention through safety systems that are capable of monitoring it. 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 relation- ships, such as the proportion of crashes involving inattention as a contributing factor (Dingus et al. 2006), or the relative and population-attributable risk associated with different inattention-related activities (Klauer et al. 2006). 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 also focused on calculating the risk associated with (human-identified) classifications of distracting tasks, such as talking, dialing, eating, and texting (e.g., Fitch et al. 2013; Klauer et al. 2006, 2010, 2014; Olson et al. 2009). Although this 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 explain why the tasks are dangerous—nor does it provide the inattention performance risk information needed for many countermeasures. It is more important to be able to determine whether the particular way a driver is doing a task (e.g., radio tuning) is dangerous, rather than simply detecting what task is being done. The radio can be tuned in a safe or unsafe way; the inattention performance quantification approach presented here focuses on being able to measure this and, in various ways, provide countermeasures based on this. Klauer et al. (2006) and Olson et al. (2009) show that criti- cal events are associated with high eyes-off-road times during the 6-second period preceding an event onset. In a reanalysis of the 100-car data, Klauer et al. (2010) showed that total Time Eyes off the forward Roadway (total TEOR) within a time period is associated with increased crash/near-crash risk. The shortest significant amounts were 20% (3 seconds) total TEOR for a 15-second task duration, or 30% (2 seconds) total TEOR for a 6-second task duration. These studies indicate that accumulated eyes-off-road time (glance history) is associated with higher crash probability, but they did not actually test independently the effect of single glance duration or assess how single glance duration combines with glance history to influence crash risk. Previous naturalistic data analyses have generally not looked at the timing aspect of eyes off road— how the temporal location of off-road glances within the time window relates to crash risk. Using the 100-car data, Liang et al. (2012) compared 24 dif- ferent ways to combine various glance characteristics, such as single glance duration, glance history, and glance location. They found that single off-road glance duration was the best crash predictor. Glance history (such as Total Glance Time) and glance location did not improve risk estimation above single glance duration but they were still predictive of crash/ near-crash risk. Further analyses of the 100-car data have

14 set out by SHRP 2 (Boyle et al. 2010). However, this research is also relevant for the “intersection crashes or other infra- structure-related crashes” priority area because Lead-Vehicle Precrash Scenarios are overrepresented in intersection crashes. The current research aims to determine the relationship between driver inattention and crash risk in Lead-Vehicle Pre- crash Scenarios. Inattention is conceptualized as a mismatch between attention and situation, in line with the recent U.S.- EU taxonomy of inattention (Engström, Monk et al. 2013). The research aims to develop inattention-risk relationships describing how an increase in inattention performance vari- ables combines with context in Lead-Vehicle Precrash Sce- narios to increase risk. The inattention-risk relationships are intended to show which glance behaviors are safer than others and pinpoint the most dangerous glances away from the road. The results aim to (1) support distraction policy, regulations, and guidelines; (2) improve intelligent vehicle safety systems; and (3) teach safe glance behaviors. Three key developments were proposed as a basis for this effort: (1) the development of observable performance-based quantifications of inattention; (2) the development of mea- sures relating inattention to event context characteristics, such as stimulus onset; and (3) the development of a validated, continuous event severity measure combining a measure of safety margin and a measure of injury risk. The main research question is this: What is the relationship between driver inattention and crash risk in Lead-Vehicle Precrash Scenarios? The specific research questions needed to answer this ques- tion are the following: • Can risk from distracting activities (secondary tasks) be explained by glance behavior? • What are the most dangerous glances away from the road, and what are safer glances? • How does the timing of lead-vehicle closing kinematics in relation to off-road glances influence crash risk? • What crash severity scale is best suited for analysis of risk? • How can we change glance behavior to be safer, and how do the results of this research translate into countermeasures? This report is structured to focus on these research ques- tions in progression. Each step in the progression of analy- sis is intended to add more detailed knowledge, going from simpler analyses to more precise analyses. The analysis starts with an examination of crashes, near crashes, and baselines in descriptive (contextual) data. Next, an analysis of the risk from distracting activities (secondary tasks) is implemented. Thereafter, a replication and extension of previous research examines risk from eyes off forward path in the period of time at the precipitating event. Next, risk is examined from eyes off forward path in the period of time leading up to the crash point (in crash events) or the mini- mum time to collision (in near-crash events). Then, the timing of Eyes off Path in relation to situation kinematics and visual cues is examined. In the final analyses, actual and potential severity is examined. Lastly, we discuss lessons learned, provide recommendations for how the results of this research can be translated into countermeasures, and identify further research needs. 1.2 Shrp 2 Naturalistic Driving Study Background The second Strategic Highway Research Program (SHRP 2) conducted the largest and most comprehensive naturalistic driving study (NDS) ever undertaken. The study collected data from over 3,000 male and female volunteer passenger- vehicle drivers, ages 16–98, during a 3-year period, with most drivers participating for 1 to 2 years. The study was conducted at a site in each of six states: Florida, Indiana, New York, North Carolina, Pennsylvania, and Washington. Data collected included vehicle speed, acceleration, and braking; vehicle controls when available; lane position; forward radar; and video views forward, to the rear, and on the driver’s face and hands. The NDS data file contains about 50 million vehicle miles, 5 million trips, more than 3,900 vehicle-years, and more than 1 million hours of video, for a total of about 2 petabytes of data. In parallel, the Roadway Information Database (RID) con- tains detailed roadway data collected on more than 12,500 centerline miles of highways in and around the study sites; about 200,000 highway miles of data from the highway inven- tories of the six study states; and additional data on crash histories, traffic and weather conditions, work zones, and ongoing safety campaigns in the study sites. The NDS and RID data can be linked to associate driving behavior with the roadway environment. Campbell (2012) provides an excellent overview of the study. Additional details may be found at the study’s InSight website (https://insight.SHRP2nds.us/). The study’s central goal is to produce unparalleled data from which to study the role of driver performance and behavior in traffic safety and how driver behavior affects the risk of crashes. This involves understanding how the driver interacts with and adapts to the vehicle, the traffic environ- ment, roadway characteristics, traffic control devices, and other environmental features. After-the-fact crash investiga- tions can do this only indirectly. The NDS data record how drivers really drive and what they are doing just before they crash or almost crash. The NDS and RID data will be used for years to come to develop and evaluate safety countermeasures designed to prevent or reduce the severity of traffic crashes and injuries.

15 First SHRP 2 NDS Analysis Projects Four contracts were awarded in 2012 under SHRP 2 Project S08, Analysis of the SHRP 2 Naturalistic Driving Study Data, to study specific research questions using the early SHRP 2 NDS and RID data. An open competition solicited proposals to address topics of the contractor’s own choosing that would have direct safety applications. The request for proposals required proposals that would • Lead to real-world applications and safety benefits (theo- retical knowledge without potential applications was not a priority); • Be broadly applicable to a substantial number of drivers, roadways, and/or vehicles in the United States; and • Demonstrate the use of the unique NDS data—similar results could not be obtained from existing nonnaturalistic data sets. In addition to these goals, SHRP 2 expected the project teams to serve as both pilot testers and advisers. As they conducted these first substantial NDS and RID analyses, these studies’ experienced researchers would discover valuable insights on a host of both pitfalls and opportunities that others should know about when they use the data. The four projects began in February 2012 and were con- ducted in two phases. In Phase 1, which concluded in Decem- ber 2012, the contractors each obtained an initial set of data, tested and refined their research plan, and developed a detailed plan for their full analyses. Three projects—of which this study, Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk, is one—successfully completed this proof-of-concept phase and were selected for the full Phase 2. The three projects obtained and analyzed a much richer though still preliminary data set and reported their results in July 2014. Constraints of the First SHRP 2 NDS Studies These initial projects were conducted while the NDS and RID data files were being built. This imposed constraints that affected the work substantially. The constraints included the following: • Sample size: In summer 2013, when the projects requested their full data sets, the NDS data file was only 20% to 30% complete. As a result, each project could obtain only a frac- tion of the trips of interest, which are now available in the full NDS data. • RID not complete and not linked to the NDS: Projects based on roads of specific types or locations could not identify those roads from the RID but instead had to use Google Earth or some similar database to identify them. They then obtained trips of interest using less efficient searches through the NDS than will be possible when the NDS and RID are linked. • Data processing: Some data, such as radar, had not been processed from their raw state to a form in which they were fully ready for analysis. • Data quality: NDS data are field data, and field data are inherently somewhat messy. At the time these projects obtained their data, some data had not been quality con- trolled and some characteristics of the data were not yet well understood. • Tools for data users: Not all crashes and near crashes had been identified, and a separate small data set containing only crashes, near crashes, and baseline exposure segments had not been built. Also, a small trip summary file contain- ing key features of each trip had not been built. Users can conduct initial analyses on many subjects quickly and easily using the trip summary file. • Other demands on data file managers: The first priority for the NDS manager, Virginia Tech Transportation Institute (VTTI), and the RID manager, Center for Transportation Research and Education (CTRE), was to complete data pro- cessing and quality control. Field data were being ingested continually. Data delivery for users sometimes was delayed due to these demands on their resources. These issues are being resolved in 2014. The NDS and RID data are complete and are being linked. Data processing and quality control are being completed. Crash and near-crash files and trip summary files are being built. If the initial three projects were to begin in 2015, each would have more data and would obtain the data far more easily and quickly. Read- ers should keep these constraints in mind as they read this report. Despite working under these constraints, this project and the other two NDS projects have produced valuable new insights on important traffic safety issues that will help reduce traffic crashes and injuries.

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TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-S08A-RW-1: Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk explores the relationship between driver inattention and crash risk in lead-vehicle precrash scenarios (corresponding to rear-end crashes).

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