3 papers
cs.RO2023
RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang, Qingwen Zhang, Thomas Gilles +2
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we pro…
cs.RO2023
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
cs.CV2023
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Yunong Wu, Thomas Gilles, Bogdan Stanciulescu +1
Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Sce…