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

翻译：预测交通参与者未来运动对于自动驾驶车辆的安全运行至关重要。我们提出了一种新颖的自监督预训练方法以及用于运动预测的 Transformer 模型。该方法基于巴洛双胞胎，将冗余减少原理应用于从高清地图生成的嵌入表示。此外，我们引入了一种新颖的冗余减少方法，将潜在的大规模、可变道路环境标记集转换为固定大小的道路环境描述符集。实验表明，所提出的预训练方法可使 minADE 和 minFDE 分别提升 12% 和 15%，在半监督设置下优于使用 PreTraM 和 SimCLR 的对比学习。我们的 REDMotion 模型在性能上与 MultiPath++ 或 Scene Transformer 等近期相关方法相当。代码地址：https://github.com/kit-mrt/road-barlow-twins