Goal-based Trajectory Prediction for improved Cross-Dataset Generalization

• URL: http://arxiv.org/abs/2507.18196v1
• Date: Thu, 24 Jul 2025 08:54:17 GMT
• Title: Goal-based Trajectory Prediction for improved Cross-Dataset Generalization
• Authors: Daniel Grimm, Ahmed Abouelazm, J. Marius Zöllner,
• Abstract summary: We introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic participants and vectorized road network.<n>We show the effectiveness of the goal selection process via cross-dataset evaluation, i.e. training on Argoverse2 and evaluating on NuScenes.
• Score: 12.233116745812898
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes a non-trivial challenge. Current SotA-models already show promising results when trained on real datasets (e.g. Argoverse2, NuScenes). Problems arise when these models are deployed to new/unseen areas. Typically, performance drops significantly, indicating that the models lack generalization. In this work, we introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic participants and vectorized road network. Latter, is used to classify goals, i.e. endpoints of the predicted trajectories, in a multi-staged approach, leading to a better generalization to unseen scenarios. We show the effectiveness of the goal selection process via cross-dataset evaluation, i.e. training on Argoverse2 and evaluating on NuScenes.

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