  Accurately predicting possible behaviors of traffic participants is an
essential capability for autonomous vehicles. Since autonomous vehicles need to
navigate in dynamically changing environments, they are expected to make
accurate predictions regardless of where they are and what driving
circumstances they encountered. Therefore, generalization capability to unseen
domains is crucial for prediction models when autonomous vehicles are deployed
in the real world. In this paper, we aim to address the domain generalization
problem for vehicle intention prediction tasks and a causal-based time series
domain generalization (CTSDG) model is proposed. We construct a structural
causal model for vehicle intention prediction tasks to learn an invariant
representation of input driving data for domain generalization. We further
integrate a recurrent latent variable model into our structural causal model to
better capture temporal latent dependencies from time-series input data. The
effectiveness of our approach is evaluated via real-world driving data. We
demonstrate that our proposed method has consistent improvement on prediction
accuracy compared to other state-of-the-art domain generalization and behavior
prediction methods.

Machine Learning;Robotics;Machine Learning