# Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

arXiv: https://arxiv.org/abs/2202.07606 · HTML: https://mlsanity.com/paper/2202.07606/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2022.

## Built on by (most cited first, 6 of 6)

- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [DECODE: Domain-aware Continual Domain Expansion for Motion Prediction](https://mlsanity.com/paper/2411.17917/) (2024)
- [H2C: Hippocampal Circuit-inspired Continual Learning for Lifelong Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2508.01158/) (2025)
- [EANet: Expert Attention Network for Online Trajectory Prediction](https://mlsanity.com/paper/2309.05683/) (2023)
- [Continual Learning for Out-of-Distribution Pedestrian Detection](https://mlsanity.com/paper/2306.15117/) (2023)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)

## Abstract (arXiv)

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In particular, we exploit online streams of pedestrian data, commonly available from the robot's detection and tracking pipeline, to refine the prediction model and its performance in unseen scenarios. To avoid the forgetting of previously learned concepts, a problem known as catastrophic forgetting, our framework includes a regularization loss to penalize changes of model parameters that are important for previous scenarios and retrains on a set of previous examples to retain past knowledge. Experimental results on real and simulation data show that our approach can improve prediction performance in unseen scenarios while retaining knowledge from seen scenarios when compared to naively training the prediction model online.