ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework

Chunnan Wang, Xiang Chen, Junzhe Wang, Hongzhi Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6563-6572

Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users. Besides, the existing works ignore Federated Learning (FL) scenarios, failing to make full use of distributed multi-source datasets with rich actual scenes to learn more a powerful TP model. In this paper, we make up for the above defects and propose ATPFL to help users federate multi-source trajectory datasets to automatically design and train a powerful TP model. In ATPFL, we build an effective TP search space by analyzing and summarizing the existing works. Then, based on the characters of this search space, we design a relation-sequence-aware search strategy, realizing the automatic design of the TP model. Finally, we find appropriate federated training methods to respectively support the TP model search and final model training under the FL framework, ensuring both the search efficiency and the final model performance. Extensive experimental results show that ATPFL can help users gain well-performed TP models, achieving better results than the existing TP models trained on the single-source dataset.