Post-interactive Multimodal Trajectory Prediction for Autonomous Driving

• URL: http://arxiv.org/abs/2503.09366v1
• Date: Wed, 12 Mar 2025 13:10:09 GMT
• Title: Post-interactive Multimodal Trajectory Prediction for Autonomous Driving
• Authors: Ziyi Huang, Yang Li, Dushuai Li, Yao Mu, Hongmao Qin, Nan Zheng,
• Abstract summary: We propose a Transformer for multimodal trajectory prediction, i.e., Pioformer.<n>It explicitly extracts the post-action features to enhance the prediction accuracy.<n>Our model has reduced the prediction errors by 4.4%, 8.4%, 14.4%, 5.7% regarding metrics minADE6, minFDE6, MR6, and brier-minFDE6, respectively.
• Score: 10.93007749660849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling the interactions among agents for trajectory prediction of autonomous driving has been challenging due to the inherent uncertainty in agents' behavior. The interactions involved in the predicted trajectories of agents, also called post-interactions, have rarely been considered in trajectory prediction models. To this end, we propose a coarse-to-fine Transformer for multimodal trajectory prediction, i.e., Pioformer, which explicitly extracts the post-interaction features to enhance the prediction accuracy. Specifically, we first build a Coarse Trajectory Network to generate coarse trajectories based on the observed trajectories and lane segments, in which the low-order interaction features are extracted with the graph neural networks. Next, we build a hypergraph neural network-based Trajectory Proposal Network to generate trajectory proposals, where the high-order interaction features are learned by the hypergraphs. Finally, the trajectory proposals are sent to the Proposal Refinement Network for further refinement. The observed trajectories and trajectory proposals are concatenated together as the inputs of the Proposal Refinement Network, in which the post-interaction features are learned by combining the previous interaction features and trajectory consistency features. Moreover, we propose a three-stage training scheme to facilitate the learning process. Extensive experiments on the Argoverse 1 dataset demonstrate the superiority of our method. Compared with the baseline HiVT-64, our model has reduced the prediction errors by 4.4%, 8.4%, 14.4%, 5.7% regarding metrics minADE6, minFDE6, MR6, and brier-minFDE6, respectively.

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