Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk (2014) / Chapter Skim
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Pages 117-125
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117 A p p e n d i x A This appendix provides more details on the actual and potential outcome severity scales described in Chapter 8. It includes a description of the methods, as well as details on assumptions that were used in the construction of these methods.
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118 Sampling Strategies in Naturalistic Driving Data The crash/near-crash dichotomy is provided as is from VTTI. There are major issues with the sampling strategies in general in naturalistic driving studies.
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119 To answer the questions above, we need to obtain appropriate distributions describing glance-off-road behavior. This is more difficult than it may seem.
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120 f(x) distributions, one placing all its mass on 10 seconds and another on 15 seconds, E[R(T)
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121 Finally, using h(t)
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122 no evasive maneuver. DeltaV is the most common approach for measuring crash severity in traditional accident investigations and correlation with injury risk (see the DeltaV section above)
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123 glance distributions at the original start of last glance off road instead of conditioning on an overlap with, for example, invTTC = 0.1 s-1 (or 0.2 s-1)
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124 a major component (kept constant in all simulations of an individual event) in the what-if simulations, the creation of LVspeed is crucial.
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125 The exclusion of events in the MIR/MCR calculation does not bias results significantly. In our MIR and MCR analysis we excluded events that did not complete the what-if simulations for a number of reasons.
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