Trajectory Prediction Method Based on Fusion of Graph Interaction and Scene Perception

Abstract

To accurately perceive the environment and predict the trajectory of the surrounding traffic participants for autonomous driving,we propose a real-time end-to-end trajectory prediction framework based on bird eye view(BEV) to learn both interaction and scene information simultaneously.The framework consists of two essential modules:graph interaction network and pyramid perception network.The former encodes the interaction patterns among traffic participants through a spatiotemporal graph convolutional network,and the latter adopts a spatiotemporal pyramid network to model the surrounding information and obtain the scene features.Next,interactive features and scene features are fused at a unified scale to perform classification and trajectory prediction tasks.Experiments and analysis on Nuscenes,a large open-source dataset,indicate that the proposed framework achieves a higher classification accuracy of 3.1% and 1.43% less predicted trajectory loss than MotionNet.Hence,our framework outperforms state-of-the-art algorithms in terms of generalization and robustness,and is more in line with perception requirements in actual autonomous driving scenes.

Keywords

• trajectory prediction|spatiotemporal graph convolutional|spatiotemporal pyramid|graph interaction encoding|feature fusion