Grimm, Daniel (FZI Research Center for Information Technology), Abouelazm, Ahmed (FZI Research Center for Information Technology), Zöllner, J. Marius (FZI Research Center for Information Technology; KIT Karlsruhe In)

Goal-Based Trajectory Prediction for Improved Cross-Dataset Generalization

Scheduled for presentation during the Regular Session "S31b-AI-Driven Motion Prediction and Safe Control for Autonomous Systems" (FR-EA-T31), Friday, November 21, 2025, 13:50−14:10, Southport 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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.