  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 of agent-class
information to guide the construction of graph adjacency matrices based on
semantic information. PG and VG create adjacency matrices to capture motion
interaction relationships between different classes agents. These graph
structures are then integrated into a mixed graph, where learning is conducted
using a spatiotemporal graph convolutional neural network (ST-GCNN). To further
enhance prediction performance, we adopt temporal convolutional networks (TCNs)
to generate the predicted trajectory with fewer parameters. Experimental
results on publicly available datasets demonstrate that SFEM-GCN surpasses
state-of-the-art methods in terms of accuracy and robustness.
