Road Barlow Twins: Redundancy Reduction for Road Environment Descriptors and Motion Prediction

Anticipating the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation. We introduce a novel self-supervised pre-training method as well as a transformer model for motion prediction. Our method is based on Barlow Twins and applies the redundancy reduction principle to embeddings generated from HD maps. Additionally, we introduce a novel approach for redundancy reduction, where a potentially large and variable set of road environment tokens is transformed into a fixed-size set of road environment descriptors (RED). Our experiments reveal that the proposed pre-training method can improve minADE and minFDE by 12% and 15% and outperform contrastive learning with PreTraM and SimCLR in a semi-supervised setting. Our REDMotion model achieves results that are competitive with those of recent related methods such as MultiPath++ or Scene Transformer. Code is available at: https://github.com/kit-mrt/road-barlow-twins