Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks

• URL: http://arxiv.org/abs/2303.04364v1
• Date: Wed, 8 Mar 2023 04:10:04 GMT
• Title: Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks
• Authors: Xing Gao, Xiaogang Jia, Yikang Li, and Hongkai Xiong
• Abstract summary: We resort to dynamic heterogeneous graphs to model the evolving scenario. We design a novel heterogeneous graphal recurrent network, aggregating diverse interaction information. With a motion forecasting decoder, our model predicts realistic and multi-modal future trajectories of agents.
• Score: 25.383615554172778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In this paper, we resort to dynamic heterogeneous graphs to model the scenario. Various scenario components including vehicles (agents) and lanes, multi-type interactions, and their changes over time are jointly encoded. Furthermore, we design a novel heterogeneous graph convolutional recurrent network, aggregating diverse interaction information and capturing their evolution, to learn to exploit intrinsic spatio-temporal dependencies in dynamic graphs and obtain effective representations of dynamic scenarios. Finally, with a motion forecasting decoder, our model predicts realistic and multi-modal future trajectories of agents and outperforms state-of-the-art published works on several motion forecasting benchmarks.

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