Learning to Predict Vehicle Trajectories with Model-based Planning

• URL: http://arxiv.org/abs/2103.04027v1
• Date: Sat, 6 Mar 2021 04:49:24 GMT
• Title: Learning to Predict Vehicle Trajectories with Model-based Planning
• Authors: Haoran Song, Di Luan, Wenchao Ding, Michael Yu Wang, and Qifeng Chen
• Abstract summary: We introduce a novel framework called PRIME, which stands for Prediction with Model-based Planning. Unlike recent prediction works that utilize neural networks to model scene context, PRIME is designed to generate accurate and feasibility-guaranteed future trajectory predictions. Our PRIME outperforms state-of-the-art methods in prediction accuracy, feasibility, and robustness under imperfect tracking.
• Score: 43.27767693429292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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