حواس پرتی راننده در رانندگان کامیون در مسافت های طولانی

Abstract Research on driver distraction has typically been conducted by means of epidemiology or experimental testing. The study presented here uses a naturalistic approach, where real-world driving data were collected from truck drivers as they worked their normal delivery runs. Crash, near-crash, and crash-relevant conflict data from 41 long-haul truck drivers, driving approximately 140,000 miles, were examined. Of the 2737 crashes, near-crashes, and crash-relevant conflicts (collectively termed “critical incidents”) that were recorded, 178 were attributed to “driver distraction”. The 178 distraction-related critical incidents were analyzed and 34 unique distraction types were identified. Results showed that a small number of long-haul drivers were involved in a disproportionate number of distraction-related critical incidents. For example, two of the drivers accounted for 43 of the 178 distraction incidents. Important insight was also gained into the relative safety impacts of different distracting agents and behaviors. The frequency and duration of a task, along with the visual demand associated with performing the task, were found to contribute in combination to the prevalence of critical incidents. Finally, it was found that simply because a task does not necessarily require visual attention does not mean that long-haul drivers will not look (sometimes often) away from the roadway. However, it is also clear that visually demanding tasks carry the highest degree of risk, relative to other categories of tasks.

1. Introduction Driver inattention occurs whenever the operator of a vehicle diverts his or her attention away from the driving task. Driver distraction, on the other hand, has been defined to occur when this inattention leads to a delay in the recognition of information that is necessary to accomplish the driving task safely (Stutts, Reinfurt, Staplin, & Rodgman, 2001b). Thus, distraction occurs when inattention leads to a critical incident. This definition describes the construct of distraction on a quantifiable basis. It also accounts for the fact that drivers often gaze at areas that are irrelevant to the driving task without any undesirable consequences. By this definition, visual inattention (and many other types of inattention, including cognitive inattention) is considered harmless until it results in a critical incident. Therefore, driver distraction can be represented as: inattention + critical incident = distraction. Using this model, studying driver distraction requires the identification of critical incidents. Critical incidents vary from high to low severity. Crashes are high severity critical incidents where there is an impact between the vehicle and another object. Low severity critical incidents include crash-relevant conflicts which involve a safety risk but where a crash does not occur. An example of a crash-relevant conflict is an unintended lane deviation in which the vehicle drifts outside the vehicle’s lane of travel. The study of distraction in the context of critical incidents has been described previously (e.g., Hancock, Lesch, & Simmons, 2003). Traditional research studying driver distraction can be classified into two broad methodological categories: epidemiology and empirical testing. Epidemiology involves looking at crashes (i.e., high severity critical incidents) after they have occurred. Researchers can use a variety of crash databases, such as the Fatal Accident Reporting System (FARS), in an attempt to assess crash causal factors. Conservative estimates based on epidemiological evidence suggest that driver distraction is a primary factor in 12.9% of all crashes (Stutts, Feaganes, Rodgman, Hamlett, Meadows, & Reinfurt, 2003a), although some of the estimates of this incidence are as high as 25–30% (Llaneras, 2000 and Minter, 2000). Epidemiological research focused on specific technologies also suggests an increased crash risk due to the distraction generated by those technologies (Goodman et al., 1997, Redelmeier and Tibshirani, 1997 and Violanti and Marshall, 1996). These data have prompted the National Highway Traffic Safety Administration (NHTSA) to study the driver distraction problem (Llaneras, 2000 and Tijerina et al., 2000). However, these databases are not sufficiently detailed to assess driver behavior. For example, the crash report from a fatal crash seldom provides any information on the driver’s behavior immediately preceding the crash. To obtain crash information of interest to driver distraction, researchers would require a substantial restructuring of the current data collection system. For example, police accident report forms could include a check box to indicate that the driver was using an electronic or telematics device (e.g., cellular phone) at the time of the crash (assuming that this could, in fact, be determined). Some of these restructuring efforts are already occurring (Model Minimum Uniform Crash Criteria, 2003). Even these changes in the crash data collection process are unlikely to provide all the necessary data for the complete assessment of driver distraction, such as eye-scanning behavior. Because driving is primarily a visual task, secondary tasks and in-vehicle devices should not significantly divert the driver’s eyes away from the forward roadway. By quantifying driver inattention, measures such as task completion time and eyes-off-road time assess the potential for distraction associated with a task or device. However, neither of these measures can be obtained from an epidemiological, post-crash data collection analysis. Empirical testing for driver distraction addresses many of the limitations associated with the epidemiological approach. For example, in a controlled simulator or test-track environment, driver behavior can be closely monitored. Typical measures of interest in driver distraction research, such as task completion time and eyes-off-road time, can be precisely measured. However, as with epidemiological studies, there are limitations to studying driver distraction using empirical methods. For example, it can be argued that the experimental situation may alter driver behavior; thus, drivers may act differently in the real world as compared to a contrived experimental world. A related problem is that many experimental studies involve data collection for a relatively short duration (e.g., up to a few hours of driving time), thereby limiting the investigation of behavior change that may occur over time. Drivers are usually asked to employ an unfamiliar technology in an unfamiliar instrumented vehicle for a relatively short time period, which may not allow the driver to become familiar with the vehicle’s control characteristics (e.g., Green, Hoekstra, & Williams, 1993) or in the case of driving simulators, may not accurately reflect driving dynamics. Altogether, while these drawbacks may be acceptable in the study of prototype devices used while driving, they are problematic in the study of everyday distractions that may serve as contributing factors for real-world crashes. Naturalistic data collection may address some of the aforementioned shortcomings of epidemiological and experimental methods. In these studies, vehicles are instrumented with a variety of data collection equipment and driven by study participants for one or more weeks (Dingus et al., 2002 and Hanowski et al., 2000) to a few months (Hanowski, Nakata, & Olson, 2004) to one year or more (Dingus et al., in press). The data collection equipment typically consists of video cameras to record driver behaviors and sensors to record driver input and performance. Naturalistic studies have been carried out in both light-vehicle (Dingus et al., in press and Stutts et al., 2003b) and commercial-vehicle (Dingus et al., 2002 and Hanowski et al., 2000) environments. With light vehicles, drivers use instrumented vehicles in their daily drives. For commercial vehicles, operators use instrumented trucks on their normal delivery, revenue-producing runs. Because data collection occurs in a real world driving environment, naturalistic driving studies tend to have limited control of independent variables. However, because the data are collected in the real world, experimental results generally have high external validity. Due to the expense, logistics, and effort associated with naturalistic data collection studies, few have been conducted. However, when naturalistic experiments are conducted, they can provide very rich data that address a variety of research areas and questions. This paper describes a naturalistic study where the data were used to investigate driver distraction in commercial vehicle operations. It was believed that the data gathered using this methodology would provide information concerning the characteristics of various distractions, including durations and frequencies, which would allow comparisons of the relative risks associated with various distracters. In addition, it was believed that this methodology would allow for the examination of relationships between the frequency of distraction incidents and driver factors.

3. Results 3.1. Distribution of distraction incidents A total of 2737 critical incidents were recorded in the Sleeper Berth study ( Dingus et al., 2002), of which 178 (6.5%) were attributed to driver distraction. The most frequent cause of critical incidents was the broad category of “judgment error”, which was the assessed cause for 2108 critical incidents (77.0%). “Other vehicle” accounted for the second largest number of incidents (265 incidents or 9.7%). “Driver distraction” was the third highest assessed cause. No crashes were recorded. Six of the 178 distraction events had no kinematics trigger event because the trigger that resulted in the data collection was timed. One event was classified as a near-crash; the associated distraction was adjusting the Citizen Band (CB) radio, which is employed by truck drivers as a two-way travel information system by using the knowledge of other drivers that are further ahead on the route (among other uses). The event was classified as a near-crash because the subject driver had to brake hard in order to avoid a crash with a lead vehicle that braked suddenly. The remaining events were classified as crash-relevant conflicts. Given this distribution and the lack of any pre-incident characteristics suggesting a difference between the near-crash and crash-relevant conflicts, all of the 178 events were considered as a group (“critical incidents”), and no distinction is made in the remainder of this paper based on the severity of an event. Driver distraction was associated with approximately 7% of the critical incidents observed in the experimental sample. Within the causes over which the truck driver had direct control, only judgment error, as previously described, was more frequent. Recall that distraction-related critical incidents occurred among 33 different drivers out of 41 drivers that comprised the driver pool in the Dingus et al. (2002) study. The incident frequency of occurrence varied substantially as a function of driver. Two of the drivers accounted for 43 (24.2%) of the distraction incidents recorded. No age or gender effects were observed. Though there were nearly equal numbers of single and team drivers represented in the distraction data set (16 single drivers and 17 team drivers), single drivers accounted for 115 of the 178 recorded distraction-related incidents (64.6%). 3.2. Critical incident analysis Table 1 displays the results of the cluster analysis, which showed separation of the thirty-four unique distraction types into seven distinct clusters. These clusters tended to separate incidents based on their duration and frequency. The frequency of occurrence of critical incidents for each Distraction Type is shown in Fig. 1, with the two drivers exhibiting the largest number of incidents shown separately. The frequency of distraction incidents in each of the clusters shown in Fig. 1 was statistically different (View the MathML sourceX16=243.9, p < 0.0001). While there were 121 incidents in the first cluster (which contained most of the tasks), there were 34 instances of the second (looking right—outside and looking at Instrument Panel, IP) and seventh (looking left—outside) events. These looking outside distractions were by far the most frequent and were grouped separately. The objects people were looking at, however, could not be identified by data analysts as camera placement did not support this type of observation. Table 1. Description of distractions and associated definitions Cluster Distraction Definition 1 Talking on CB Driver is holding CB to mouth and talking; usually looking forward; one hand off the wheel Looking at CB Driver is looking up at CB receiver located on ceiling at the front and center of cab; both hands on the wheel Adjusting CB Driver is adjusting knobs, with right arm extended up, on CB receiver located on ceiling at the front and center of cab; glancing at CB periodically; one hand off the wheel Looking at radio Driver is looking at the music radio, down and to the right, on center console of cab; both hands on the wheel Adjusting radio Driver is reaching to the music radio, on center console of cab, adjusting station or volume; glancing at radio periodically; one hand off the wheel Looking up Driver is looking up at the visor; both hands on the wheel Looking down Driver is looking down; either in lap at something unknown, or at hands; may have one or both hands off the wheel Looking at floor Driver is looking at/for something on the floor (down and to the right); both hands on the wheel Talking to passenger Driver is talking to another person in the cab; sometimes looking to the right at passenger; both hands on the wheel Eating/talking Driver is eating food and looking at passenger; one hand off the wheel Toothpick/visor mirror Driver is looking up in the visor mirror, while picking teeth with a toothpick; one hand off the wheel Drinking Driver is drinking out of a soda bottle or mug; usually looking forward; one hand off the wheel Getting cigarette Driver is removing a cigarette from rest of pack; often looking at pack; one hand off the wheel Lighting cigarette Driver is lighting a cigarette; often looking at cigarette; one or both hands off the wheel Blowing smoke Driver has head turned, blowing smoke out the window; usually holding cigarette with one hand off the wheel Adjusting in seat Driver is adjusting himself/herself in driver seat; usually looking forward; both hands on the wheel Reaching to floor Driver is reaching for something either on the floor of the cab (down and to the right) or somewhere in the cab; usually looking forward; takes one hand off the wheel Rubbing face Driver is wiping face off or rubbing eyes; usually looking forward but eyes may close for a few moments; one hand off the wheel Taking off jacket Driver is taking off jacket; usually looking forward; one hand off the wheel Brushing hair Driver is using a hairbrush to brush hair; looking forward; one hand off the wheel Wiping dash Driver is wiping off dash of cab with a cloth; usually looking at dash; one hand off the wheel Release of steering wheel Driver is looking forward but does not have either hand on wheel while moving in seat; is not holding anything Answering ringing cell phone/Looking at cell phone display Driver is answering ringing cell phone; reaches to middle console, picks up phone, looks down at phone several times, but never puts it to ear; one hand off the wheel 2 Looking right—outside Driver has head turned to the right, either looking in passenger side mirror, or out passenger window; usually both hands are on the wheel Looking at IP Driver is looking down, through steering wheel, at instrument panel containing speedometer and gauges; both hands on the wheel 3 Dialing cell phone Driver is looking down at cell phone in hands, dialing number; one hand off the wheel Plugging in cell phone Driver is plugging in battery charger to bottom of cell phone; usually looking at the phone; one hand off the wheel Getting food Driver is getting food out of a bag in their lap; often looking at bag/food; one or both hands off the wheel Looking at paperwork Driver is holding paperwork on steering wheel and is looking down at it; one or both hands off the wheel 4 Reaching in pocket Driver is reaching for something in either front shirt pocket, or back pant pocket; usually looking forward but moving around in seat; one hand off the wheel Looking outside Driver is looking at a road sign, something along side of the road, or another car, but is still looking out front window; both hands on the wheel 5 Talking on cell phone Driver is holding cell phone up to ear and talking on it; usually looking forward; one hand off the wheel 6 Phone call/hanging up cell phone Driver makes phone call and is hanging up cell phone; reaches down to floor to put phone back; usually looks down; one hand off the wheel 7 Looking left—outside Driver has head turned to the left, either looking in driver side mirror, or out driver window; usually both hands are on the wheel The definition for each distraction highlights the relevant task/activity and indicates where the driver tended to look during the distraction (the associated visual demand) and the typical status of the driver’s hands on the steering wheel during the distraction (the associated manual demand). Groupings obtained from the cluster analysis are also indicated. Table options Frequency of critical incidents for each of the 34 distraction types. Totals for ... Fig. 1. Frequency of critical incidents for each of the 34 distraction types. Totals for each of the two drivers with the most incidents are shown relative to the rest of the drivers. Vertical lines separate incidents in the different categories defined by the cluster analysis. Figure options Once these distractions were identified, further analysis of their occurrences was performed to identify common factors that suggested reasons for the link between these events and a critical incident. Fig. 2 displays the duration of the activity prior to the occurrence of the critical incident and the proportion of time that the driver’s eyes were off the road for a 20-s period around the incident as a function of cluster and associated distracter. The one-way ANOVA showed that the average duration of a distracter, F(6,27) = 10.42, p < 0.0001, and the proportion of eyes-off-road time, F(6,27) = 19.32, p < 0.0001, were significantly different across clusters. Task duration clearly differentiated between two of the cell phone tasks, exhibiting the longest durations, and the remaining tasks. Results of the Tukey HSD procedure indicated that the mean duration of distracters in Clusters 5 and 6 (cell phone tasks, M = 190.9 s) was significantly different than the duration of distracters in the remaining clusters (M = 15.61 s). However, Tukey HSD tests based on the proportion of eyes-off-road time showed few clear-cut differences; distracters in Cluster 3 (M = 58.6%), which required more than a 50% proportion of eyes-off-road time, were significantly different from distracters in Clusters 1, 4, and 5 (M = 23.9%). In general, the average length of the distractions varied greatly and did not correspond with the relative frequency of the occurrence of the critical events. It is important to note that the critical event frequencies vary greatly across these categories, and several have only a single data point. However, since this figure represents only safety-critical event data in which a distracting agent was an apparent contributing factor, it provides important insight into the characteristics of the pre-event behavior that led to the event. Mean duration and proportion of eyes-off-road time for each of the 34 ... Fig. 2. Mean duration and proportion of eyes-off-road time for each of the 34 distraction types. Vertical lines separate incidents in the different categories defined by the cluster analysis. Uppercase letters indicate differences between clusters based on the proportion of eyes-off-road time (clusters with the same letter are not significantly different). Lowercase letters indicate differences between clusters based on the distracter’s duration (clusters with the same letter are not significantly different). Figure options Fig. 3 illustrates the mean glance durations for viewing locations away from the forward roadway. The one-way ANOVA showed a significant difference in the mean downward glance duration between clusters, F(6,27) = 15.13, p < 0.0001. Results from the Tukey HSD procedure indicated that Cluster 4 (M = 0.00 s) had a significantly lower mean downward glance duration than the remaining clusters (M = 1.00 s, 1.36 s, 1.34 s, 0.93 s, 1.23 s, and 1.08 s for Clusters 1–3 and 5–7, respectively). This was expected given that the tasks in Cluster 4 had no downward glances associated with them, thus, their mean downward glance duration was zero. The overall mean downward glance duration across clusters was 1.01 s (SD = 0.33 s). Maximum single glance durations were generally shorter than four seconds, with two exceptions, Clusters 2 and 7. The overall maximum task duration, however, was contained within Cluster 3 and occurred while a participant was looking at paperwork. Mean and maximum downward glance duration for each of the 34 distraction types. ... Fig. 3. Mean and maximum downward glance duration for each of the 34 distraction types. Vertical lines separate incidents in the different categories defined by the cluster analysis. Uppercase letters indicate differences between clusters for the mean downward glance duration (clusters with the same letter are not significantly different).