  Efficient driving in urban traffic scenarios requires foresight. The
observation of other traffic participants and the inference of their possible
next actions depending on the own action is considered cooperative prediction
and planning. Humans are well equipped with the capability to predict the
actions of multiple interacting traffic participants and plan accordingly,
without the need to directly communicate with others. Prior work has shown that
it is possible to achieve effective cooperative planning without the need for
explicit communication. However, the search space for cooperative plans is so
large that most of the computational budget is spent on exploring the search
space in unpromising regions that are far away from the solution. To accelerate
the planning process, we combined learned heuristics with a cooperative
planning method to guide the search towards regions with promising actions,
yielding better solutions at lower computational costs.
