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.
Learning to Predict Vehicle Trajectories with Model-based Planning
Haoran Song,Di Luan,Wenchao Ding,M. Wang,Qifeng Chen
Published 2021 in Conference on Robot Learning
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- Publication year
2021
- Venue
Conference on Robot Learning
- Publication date
2021-03-06
- Fields of study
Computer Science, Engineering
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