  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, which guarantees the
trajectory feasibility by exploiting a model-based generator to produce future
trajectories under explicit constraints and enables accurate multimodal
prediction by using a learning-based evaluator to select future trajectories.
We conduct experiments on the large-scale Argoverse Motion Forecasting
Benchmark. Our PRIME outperforms state-of-the-art methods in prediction
accuracy, feasibility, and robustness under imperfect tracking. Furthermore, we
achieve the 1st place on the Argoervese Leaderboard.
