Learning to Predict Vehicle Trajectories with Model-based Planning
This addresses the need for safe and reliable trajectory prediction in autonomous driving, representing a strong specific gain rather than a foundational advance.
The paper tackles the problem of predicting vehicle trajectories for autonomous driving by introducing PRIME, a framework that guarantees feasibility and improves accuracy, achieving state-of-the-art results on the Argoverse benchmark.
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.