PBP: Path-based Trajectory Prediction for Autonomous Driving

TL;DR

This paper introduces Path-based Prediction (PBP), a novel trajectory prediction method for autonomous driving that improves map compliance by predicting over reference paths and using path-relative frames.

Contribution

PBP enhances goal-based trajectory prediction by incorporating reference path probabilities and path-relative frames, leading to better map adherence.

Findings

PBP achieves competitive accuracy on standard metrics.

PBP significantly outperforms baselines in map compliance.

Results are validated on the Argoverse dataset.

Abstract

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.