  In this paper we consider the application of Stackelberg game theory to model
discretionary lane-changing in lightly congested highway setting. The
fundamental intent of this model, which is parameterized to capture driver
disposition (aggressiveness or inattentiveness), is to help with the
development of decision-making strategies for autonomous vehicles in ways that
are mindful of how human drivers perform the same function on the road (on
which have reported elsewhere.) This paper, however, focuses only on the model
development and the respective qualitative assessment. This is accomplished in
unit test simulations as well as in bulk mode (i.e. using the Monte Carlo
methodology), via a limited traffic micro-simulation compared against the NHTSA
100-Car Naturalistic Driving Safety data. In particular, a qualitative
comparison shows the relative consistency of the proposed model with human
decision-making in terms of producing qualitatively similar proportions of
crashes and near crashes as a function of driver inattentiveness (or
aggressiveness). While this result by itself does not offer a true quantitative
validation of the proposed model, it does demonstrate the utility of the
proposed approach in modeling discretionary lane-changing and may therefore be
of use in autonomous driving in a manner that is consistent with human decision
making on the road.
