RedMotion: Motion Prediction via Redundancy Reduction
Primary Area: applications to robotics, autonomy, planning
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Keywords: Motion prediction, self-supervised learning, trajectory forecasting, self-driving
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TL;DR: Transformer model for motion prediction in self-driving that incorporates two types of redundancy reduction
Abstract: Predicting the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation.
We introduce RedMotion, a transformer model for motion prediction that incorporates two types of redundancy reduction.
The first type of redundancy reduction is induced by an internal transformer decoder and reduces a variable-sized set of road environment tokens, such as road graphs with agent data, to a fixed-sized embedding.
The second type of redundancy reduction is a self-supervised learning objective and applies the redundancy reduction principle to embeddings generated from augmented views of road environments.
Our experiments reveal that our representation learning approach can outperform PreTraM, Traj-MAE, and GraphDINO in a semi-supervised setting.
Our RedMotion model achieves results that are competitive with those of Scene Transformer or MTR++.
We provide an anonymized open source implementation that is accessible via Colab: https://colab.research.google.com/drive/16pwsmOTYdPpbNWf2nm1olXcx1ZmsXHB8
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Submission Number: 9187
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