  Trajectory prediction plays a crucial role in the autonomous driving stack by
enabling autonomous vehicles to anticipate the motion of surrounding agents.
Goal-based prediction models have gained traction in recent years for
addressing the multimodal nature of future trajectories. Goal-based prediction
models simplify multimodal prediction by first predicting 2D goal locations of
agents and then predicting trajectories conditioned on each goal. However, a
single 2D goal location serves as a weak inductive bias for predicting the
whole trajectory, often leading to poor map compliance, i.e., part of the
trajectory going off-road or breaking traffic rules. In this paper, we improve
upon goal-based prediction by proposing the Path-based prediction (PBP)
approach. PBP predicts a discrete probability distribution over reference paths
in the HD map using the path features and predicts trajectories in the
path-relative Frenet frame. We applied the PBP trajectory decoder on top of the
HiVT scene encoder and report results on the Argoverse dataset. Our experiments
show that PBP achieves competitive performance on the standard trajectory
prediction metrics, while significantly outperforming state-of-the-art
baselines in terms of map compliance.
