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 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 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 inattention. 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, nonspecific 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 distraction 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 intelligent 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 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 relationships, 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, dialling, 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 critical 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 different 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 revealed that risk is pinpointed to the timing of off-road glances in relation to external events. Risk is primarily associated 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 leading 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 behaviour 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 pre-crash situations. The data are 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. This S08A research targets two of the highest prioritised 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, behaviour, and performance”–one of the four priority areas set out by SHRP 2 (Boyle et al. 2010). However, this research is also relevant for the “intersection crashes or other infrastructure-related crashes” priority area because Lead-Vehicle Pre-crash 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 conceptualised 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 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. The results aim to (1) support distraction policy, regulations, and guidelines; (2) improve intelligent vehicle safety systems; and (3) teach safe glance behaviours. 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 measures 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 Pre-crash Scenarios? The specific research questions needed to answer this question are the following: ??Can risk from distracting activities (secondary tasks) be explained by glance behaviour? ??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 behaviour to be safer, and how do the results of this research translate into countermeasures? This report is structured to focus on these research questions in progression. Each step in the progression of analysis 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 minimum 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. (Author/publisher)
Abstract