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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction by Du, Quancheng; Wang, Xiao; Yin, Shouguo; Li, Lingxi; Ning, Huansheng is a scholarly article available to read on EtoBox.

What is Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction about?

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding

Author

Du, Quancheng; Wang, Xiao; Yin, Shouguo; Li, Lingxi; Ning, Huansheng

Published

2024

Language

EN