Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk (2014) / Chapter Skim
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Pages 87-101
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87 C h a p t e r 8 The previous analyses have focused on crash-contributing factors in Lead-Vehicle Precrash Scenarios, including descriptive variables, distracting activities, glance behavior, situation kinematics, and visual cues. The focus has been on quantifying crash risk rather than injury risk.
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88 naturalistic driving data. The aim is not to provide definitive answers but rather to show a proof of concept.
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89 • The choice of a driver reaction time -- that is, time from looking back on the road until applying full brake. Conclusion 3 under Section 7.6 indicated that the results show that a fixed reaction time is not correct.
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90 (slopes) represent successful evasive maneuvers (where there was no crash)
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91 In the second panel from the right, MCR values for the individual crashes and near crashes are shown. The scale is clearly continuous for both crashes and near crashes.
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92 (e.g., availability of speed)
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93 on the road. The bar at zero is 79% of the total distribution (histogram)
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94 40 seconds to complete. The RelativeRiskTask_vs_task of the two tasks can be calculated using the following equation, where the mean MIR of the matched baseline glance distribution is subtracted from the easy and difficult tasks' mean MIRs, and the relative risk is scaled by the difference in Total Glance Time.
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95 probability of glances of 1.25-second duration in the difficult task compared with the easy task, and vice versa for 0.75 seconds. Longer glances in either task would increase the MIR for that task (compare with Figure 8.3)
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96 8.7 Generalization analysis: extreme Value analysis This section discusses results of models using extreme value theory to extrapolate from crashes to high-severity (extreme) DeltaV values and from near crashes to crash risk.
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97 We also used extreme value analysis (EVA) to estimate the tail of the impact-speed distribution for crashes.
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98 Figure 8.10. Return level graph for Generalized Pareto model of impact speed in crashes.
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99 resulting in higher MIR and MCR indices. It may be that remedies for these situations are what is needed.
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100 The third example addressed the representativeness of the sample of crashes and near crashes. This was done by comparing DeltaV from accident statistics (NASS-CDS crash database)
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101 While success or failure to avoid is important for understanding avoidance itself, the outcome of an event does not, in itself, determine the inherent risk of crashing in that event. Another use of the approach is to evaluate the consequences of different task-related glance distributions on a large set of crashes and near crashes.
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