  Accurate vehicle trajectory prediction is an unsolved problem in autonomous
driving with various open research questions. State-of-the-art approaches
regress trajectories either in a one-shot or step-wise manner. Although
one-shot approaches are usually preferred for their simplicity, they relinquish
powerful self-supervision schemes that can be constructed by chaining multiple
time-steps. We address this issue by proposing a middle-ground where multiple
trajectory segments are chained together. Our proposed Multi-Branch
Self-Supervised Predictor receives additional training on new predictions
starting at intermediate future segments. In addition, the model 'imagines' the
latent context and 'predicts the past' while combining multi-modal trajectories
in a tree-like manner. We deliberately keep aspects such as interaction and
environment modeling simplistic and nevertheless achieve competitive results on
the INTERACTION dataset. Furthermore, we investigate the sparsely explored
uncertainty estimation of deterministic predictors. We find positive
correlations between the prediction error and two proposed metrics, which might
pave way for determining prediction confidence.
