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Found 4 papers, 3 papers with code
Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios.
Passage Segmentation of Documents for Extractive Question Answering
Retrieval-Augmented Generation (RAG) has proven effective in open-domain question answering.
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
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