  Safe interaction with human drivers is one of the primary challenges for
autonomous vehicles. In order to plan driving maneuvers effectively, the
vehicle's control system must infer and predict how humans will behave based on
their latent internal state (e.g., intentions and aggressiveness). This
research uses a simple model for human behavior with unknown parameters that
make up the internal states of the traffic participants and presents a method
for quantifying the value of estimating these states and planning with their
uncertainty explicitly modeled. An upper performance bound is established by an
omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of
the internal states. A baseline lower bound is established by planning with
MCTS assuming that all drivers have the same internal state. MCTS variants are
then used to solve a partially observable Markov decision process (POMDP) that
models the internal state uncertainty to determine whether inferring the
internal state offers an advantage over the baseline. Applying this method to a
freeway lane changing scenario reveals that there is a significant performance
gap between the upper bound and baseline. POMDP planning techniques come close
to closing this gap, especially when important hidden model parameters are
correlated with measurable parameters.
