Towards Single Source Domain Generalisation in Trajectory Prediction: A Motion Prior based Approach

Trajectory prediction is an important task in many real-world applications. However, data-driven approaches typically suffer from dramatic performance degradation when applied to unseen environments due to the inevitable domain shift brought by changes in factors such as pedestrian walking speed, the geometry of the environment, etc. In particular, when a dataset does not contain sufficient samples to determine prediction rules, the trained model can easily consider some important features as domain variant. We propose a framework that integrates a simple motion prior with deep learning to achieve, for the first time, exceptional single-source domain generalisation for trajectory prediction, in which deep learning models are only trained using a single domain and then applied to multiple novel domains. Instead of predicting the exact future positions directly from the model, we first assign a constant velocity motion prior to each pedestrian and then learn a conditional trajectory prediction model to predict residuals to the motion prior using auxiliary information from the surrounding environment. This strategy combines deep learning models with knowledge priors to simultaneously simplify training and enhance generalisation, allowing the model to focus on disentangling data-driven spatio-temporal factors while not overfitting to individual motions. We also propose a novel Train-on-Best-Motion strategy that can alleviate the adverse effects of domain shift, brought on by changes in environment, by exploiting invariances inherent to the choice of motion prior. Experiments across multiple datasets of different domains demonstrate that our approach reduces the influence of domain shift and also generalizes better to unseen environments.