  Autonomous agents that drive on roads shared with human drivers must reason
about the nuanced interactions among traffic participants. This poses a highly
challenging decision making problem since human behavior is influenced by a
multitude of factors (e.g., human intentions and emotions) that are hard to
model. This paper presents a decision making approach for autonomous driving,
focusing on the complex task of merging into moving traffic where uncertainty
emanates from the behavior of other drivers and imperfect sensor measurements.
We frame the problem as a partially observable Markov decision process (POMDP)
and solve it online with Monte Carlo tree search. The solution to the POMDP is
a policy that performs high-level driving maneuvers, such as giving way to an
approaching car, keeping a safe distance from the vehicle in front or merging
into traffic. Our method leverages a model learned from data to predict the
future states of traffic while explicitly accounting for interactions among the
surrounding agents. From these predictions, the autonomous vehicle can
anticipate the future consequences of its actions on the environment and
optimize its trajectory accordingly. We thoroughly test our approach in
simulation, showing that the autonomous vehicle can adapt its behavior to
different situations. We also compare against other methods, demonstrating an
improvement with respect to the considered performance metrics.
