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60 Figure 7.6. Range with and without target tracking. face. Some projects added a fifth camera to capture the driver's glare. When some data from outside sources are unavailable, hand and foot movements. such as the weather data and level of congestion, the ability to Figure 7.7 illustrates the camera views in Project 7 (2). In acquire them through video data reduction becomes vital. If the UMTRI studies, only two cameras were used, providing a the video data are blurred, it is impossible to obtain such data. front view and a driver's face view, because less emphasis was In the new DAS protocol of VTTI, the problem is solved by put on observing driver behavior. Video data that does not using Autobrite, as shown in Figure 7.9 (4). As can be seen, the include all four views have limited usage in this study, given quality of video data on the right of the figure is significantly that it is not possible to identify causes and behavior before improved. crash or near-crash events. In making decisions based on driver behavior, as is the case Reduced Data in this research effort, a prerequisite is satisfactory quality of video data. Incorrect brightness is a typical problem that pre- One challenge faced by researchers is data reduction in which vents researchers from interpreting video data clearly. raw data can be organized in a more functional format. Each Figure 7.8 shows some video captures in Project 5 (3). Dur- study listed previously has a data reduction dictionary into ing some daytime driving, when the vehicle is heading directly which raw data were coded by reductionists, but the coding into a setting sun, the camera images degraded because of sun schemes of each dictionary are not identical. In Project 2, the Camera 1 Camera 3 Behind Vehicle Camera 2 Front of Vehicle Camera 4 Figure 7.7. Camera directions and approximate fields of view in Project 7.
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61 Figure 7.8. Simultaneous images from the cameras of an RDCWS vehicle heading into the sun. variable "Time of Day" was coded in 0 or 1 for Day or Night, For example, "Driver Actions/Factors/Behaviors Relating to respectively. The "Location of eyes at time of the alert" was Event" described drivers' emotions, coded in 60 values to coded 0 through 9, representing "Looking forward at forward represent "angry," "drowsy," "drunk," and others. When using scene" at one extreme, to the more distracted statuses of "Head data from multiple sources with different coding protocols, a down, looking at center stack console area" or "Cannot accu- proper unifying process is required to ensure the same stan- rately evaluate eye location." In contrast, data reduction at dards in data analysis. VTTI was more extensive. In a typical VTTI study (such as Proj- Reductionist capability is a dominant factor that affects the ect 7 and Project 8), "Date," "Day of Week," and "Time" were quality of reduced data. VTTI has a professional data reduc- three independently coded variables to pinpoint the time of an tion team composed of professional researchers and graduate event. "Light Condition" was a separate variable, in addition to research assistants and a separate data reduction laboratory led time and day variables, coded from 01 to 05 to describe the light by laboratory managers. All the reductionists have extensive situation as "Daylight," "Dark," "Dark but lighted," "Dawn," experience in data reduction and analysis. Before data reduction or "Dusk." VTTI studies coded driver actions and distrac- officially starts, reductionists are trained using a protocol writ- tions more elaborately. "Driver Potentially Distracting Driver ten by the laboratory manager and project researchers. The lab- Behavior" was a variable coded in 31 values describing situa- oratory manager works closely with the reductionists to assess tions including "bite nails," "remove/adjust jewelry," and their comprehension of the data reduction dictionary. UMTRI even "comb/brush/fix hair." Besides behavior variables, some also has a professional data reduction team with researchers and variables were designed to describe other statuses of drivers. graduate students. Students are responsible for relatively easy variable coding, such as weather condition, presence of passen- ger, and type of road. The research staff is responsible for more difficult variables that require judgments, such as degree of dis- traction and behavior coding. Quality control in data reduction is critical in data post- processing and can be a decisive factor in the success of later analyses. A quality control procedure to support accurate and consistent coding was established at VTTI. For example, in Project 11 data reductionists performed 30 min of spot checks of their own or other reductionists' work each week. Besides the spot checks, inter- and intra-rater reliability tests were conducted every 3 months. Reliability tests were developed for which the reductionist was required to make validity judg- ments for 20 events. Three of the 20 events were also com- pletely reduced; in other words, the reductionist recorded information for all reduction variables as opposed to sim- ply marking the severity of the event. These three tests were Figure 7.9. Prototype DAS camera under consideration. repeated on the next test to obtain a measure of intra-rater