  Predicting the future trajectories of on-road vehicles is critical for
autonomous driving. In this paper, we introduce a novel prediction framework
called PRIME, which stands for Prediction with Model-based Planning. Unlike
recent prediction works that utilize neural networks to model scene context and
produce unconstrained trajectories, PRIME is designed to generate accurate and
feasibility-guaranteed future trajectory predictions. PRIME guarantees the
trajectory feasibility by exploiting a model-based generator to produce future
trajectories under explicit constraints and enables accurate multimodal
prediction by utilizing a learning-based evaluator to select future
trajectories. We conduct experiments on the large-scale Argoverse Motion
Forecasting Benchmark, where PRIME outperforms the state-of-the-art methods in
prediction accuracy, feasibility, and robustness under imperfect tracking.
