Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion (2011) / Chapter Skim
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A higher frequency generates data sets that are larger in size and brings more challenge to data reduction. When postprocessing and analyzing the same type of data from different sources, as was done for this study, special attention should be paid to the differences in data collection rates.
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57Data Acquisition System Module Cell phone detect Comment PB Cellular antenna Steering wheel angle Inertial sensors Driver face camera Front view camera Audio microphone Figure 7.1. DAS used in Project 2 and Project 5.Figure 7.2.
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When reviewing and reducing radar data, one must consider the type of radar used, the rate of data collection, how "noisy" the data are, and the assumptions used to mathematically smooth the data. One typically derived variable was the TTC between the subject vehicle and a leading vehicle.
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59-10 -5 0 5 15 20 25 30 Sp ee d (m /s )
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Incorrect brightness is a typical problem that prevents researchers from interpreting video data clearly. Figure 7.8 shows some video captures in Project 5 (3)
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Prototype DAS camera under consideration.variable "Time of Day" was coded in 0 or 1 for Day or Night, respectively. The "Location of eyes at time of the alert" was coded 0 through 9, representing "Looking forward at forward scene" at one extreme, to the more distracted statuses of "Head down, looking at center stack console area" or "Cannot accurately evaluate eye location." In contrast, data reduction at VTTI was more extensive.
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The researchers then jointly viewed and recoded all video to modify factors that had not been agreed on. Each of these meticulous steps guarantees that the data reduction is under control.
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University of Michigan Transportation Research Institute. Automotive Collision Avoidance System Field Operational Test Report: Methodology and Results.
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