  Prior arts in the field of motion predictions for autonomous driving tend to
focus on finding a trajectory that is close to the ground truth trajectory.
Such problem formulations and approaches, however, frequently lead to loss of
diversity and biased trajectory predictions. Therefore, they are unsuitable for
real-world autonomous driving where diverse and road-dependent multimodal
trajectory predictions are critical for safety. To this end, this study
proposes a novel loss function, \textit{Lane Loss}, that ensures map-adaptive
diversity and accommodates geometric constraints. A two-stage trajectory
prediction architecture with a novel trajectory candidate proposal module,
\textit{Trajectory Prediction Attention (TPA)}, is trained with Lane Loss
encourages multiple trajectories to be diversely distributed, covering feasible
maneuvers in a map-aware manner. Furthermore, considering that the existing
trajectory performance metrics are focusing on evaluating the accuracy based on
the ground truth future trajectory, a quantitative evaluation metric is also
suggested to evaluate the diversity of predicted multiple trajectories. The
experiments performed on the Argoverse dataset show that the proposed method
significantly improves the diversity of the predicted trajectories without
sacrificing the prediction accuracy.
