Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting
<|reference_start|>Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting: Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than simple rule-based models. To address this limitation, we propose a novel framework, Adaptive Prediction Ensemble (APE), which integrates deep learning and rule-based prediction experts. A learned routing function, trained concurrently with the deep learning model, dynamically selects the most reliable prediction based on the input scenario. Our experiments on large-scale datasets, including Waymo Open Motion Dataset (WOMD) and Argoverse, demonstrate improvement in zero-shot generalization across datasets. We show that our method outperforms individual prediction models and other variants, particularly in long-horizon prediction and scenarios with a high proportion of OOD data. This work highlights the potential of hybrid approaches for robust and generalizable motion prediction in autonomous driving.<|reference_end|>
@article{li2024adaptive,
    title={Adaptive Prediction Ensemble: Improving Out-of-Distribution
  Generalization of Motion Forecasting},
    author={Jinning Li and Jiachen Li and Sangjae Bae and David Isele},
    journal={arXiv preprint arXiv:2407.09475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09475},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}