  Inspired by recent developments regarding the application of self-supervised
learning (SSL), we devise an auxiliary task for trajectory prediction that
takes advantage of map-only information such as graph connectivity with the
intent of improving map comprehension and generalization. We apply this
auxiliary task through two frameworks - multitasking and pretraining. In either
framework we observe significant improvement of our baseline in metrics such as
$\mathrm{minFDE}_6$ (as much as 20.3%) and $\mathrm{MissRate}_6$ (as much as
33.3%), as well as a richer comprehension of map features demonstrated by
different training configurations. The results obtained were consistent in all
three data sets used for experiments: Argoverse, Interaction and NuScenes. We
also submit our new pretrained model's results to the Interaction challenge and
achieve $\textit{1st}$ place with respect to $\mathrm{minFDE}_6$ and
$\mathrm{minADE}_6$.
