Computer Science > Computer Vision and Pattern Recognition
[Submitted on 7 Sep 2023 (v1), last revised 2 Mar 2024 (this version, v2)]
Title:PBP: Path-based Trajectory Prediction for Autonomous Driving
View PDF HTML (experimental)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.
Submission history
From: Henggang Cui [view email][v1] Thu, 7 Sep 2023 14:45:41 UTC (3,284 KB)
[v2] Sat, 2 Mar 2024 20:54:01 UTC (3,282 KB)
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