  Modeling multi-modal high-level intent is important for ensuring diversity in
trajectory prediction. Existing approaches explore the discrete nature of human
intent before predicting continuous trajectories, to improve accuracy and
support explainability. However, these approaches often assume the intent to
remain fixed over the prediction horizon, which is problematic in practice,
especially over longer horizons. To overcome this limitation, we introduce
HYPER, a general and expressive hybrid prediction framework that models
evolving human intent. By modeling traffic agents as a hybrid
discrete-continuous system, our approach is capable of predicting discrete
intent changes over time. We learn the probabilistic hybrid model via a maximum
likelihood estimation problem and leverage neural proposal distributions to
sample adaptively from the exponentially growing discrete space. The overall
approach affords a better trade-off between accuracy and coverage. We train and
validate our model on the Argoverse dataset, and demonstrate its effectiveness
through comprehensive ablation studies and comparisons with state-of-the-art
models.
