Self-Supervised Continual Learning for Interaction-Aware Pedestrian Prediction Models

Learning human motion prediction models online is key for autonomous navigation in unknown dynamic scenarios. Previous works focus solely on improving prediction network architectures, whilst training them offline. This paper introduces a self-supervised continual learning framework that continuously improves data-driven pedestrian trajectory...