  We propose a multi-agent based computational framework for modeling
decision-making and strategic interaction at micro level for smart vehicles in
a smart world. The concepts of Markov game and best response dynamics are
heavily leveraged. Our aim is to make the framework conceptually sound and
computationally practical for a range of realistic applications, including
micro path planning for autonomous vehicles. To this end, we first convert the
would-be stochastic game problem into a closely related deterministic one by
introducing risk premium in the utility function for each individual agent. We
show how the sub-game perfect Nash equilibrium of the simplified deterministic
game can be solved by an algorithm based on best response dynamics. In order to
better model human driving behaviors with bounded rationality, we seek to
further simplify the solution concept by replacing the Nash equilibrium
condition with a heuristic and adaptive optimization with finite look-ahead
anticipation. In addition, the algorithm corresponding to the new solution
concept drastically improves the computational efficiency. To demonstrate how
our approach can be applied to realistic traffic settings, we conduct a
simulation experiment: to derive merging and yielding behaviors on a
double-lane highway with an unexpected barrier. Despite assumption differences
involved in the two solution concepts, the derived numerical solutions show
that the endogenized driving behaviors are very similar. We also briefly
comment on how the proposed framework can be further extended in a number of
directions in our forthcoming work, such as behavioral calibration using real
traffic video data, computational mechanism design for traffic policy
optimization, and so on.
