  This paper introduces a method to compute a sparse lattice planner control
set that is suited to a particular task by learning from a representative
dataset of vehicle paths. To do this, we use a scoring measure similar to the
Fr\'echet distance and propose an algorithm for evaluating a given control set
according to the scoring measure. Control actions are then selected from a
dense control set according to an objective function that rewards improvements
in matching the dataset while also encouraging sparsity. This method is
evaluated across several experiments involving real and synthetic datasets, and
it is shown to generate smaller control sets when compared to the previous
state-of-the-art lattice control set computation technique, with these smaller
control sets maintaining a high degree of manoeuvrability in the required task.
This results in a planning time speedup of up to 4.31x when using the learned
control set over the state-of-the-art computed control set. In addition, we
show the learned control sets are better able to capture the driving style of
the dataset in terms of path curvature.
