The addition of user-customizable features to automobiles increases the need to differentiate among drivers so that each driver’s custom settings can be automatically applied. Part 1 of this study modeled driver component positioning as a function of the stature difference between sharing drivers. To fully understand the feasibility of this approach to driver identification, we need to model the distribution of stature differences in the population of sharing drivers. Monte Carlo simulation is used to simulate both population variability in stature and positioning and the effect of initial conditions on positioning are included. The simulation of 10,000 households showed that for 87% of target pairs, differentiation performance of fewer than 2% errors can be achieved, even when the drivers share a vehicle equally (the most difficult differentiation scenario).

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