  To drive safely in complex traffic environments, autonomous vehicles need to
make an accurate prediction of the future trajectories of nearby heterogeneous
traffic agents (i.e., vehicles, pedestrians, bicyclists, etc). Due to the
interactive nature, human drivers are accustomed to infer what the future
situations will become if they are going to execute different maneuvers. To
fully exploit the impacts of interactions, this paper proposes a ego-planning
guided multi-graph convolutional network (EPG-MGCN) to predict the trajectories
of heterogeneous agents using both historical trajectory information and ego
vehicle's future planning information. The EPG-MGCN first models the social
interactions by employing four graph topologies, i.e., distance graphs,
visibility graphs, planning graphs and category graphs. Then, the planning
information of the ego vehicle is encoded by both the planning graph and the
subsequent planning-guided prediction module to reduce uncertainty in the
trajectory prediction. Finally, a category-specific gated recurrent unit
(CS-GRU) encoder-decoder is designed to generate future trajectories for each
specific type of agents. Our network is evaluated on two real-world trajectory
datasets: ApolloScape and NGSIM. The experimental results show that the
proposed EPG-MGCN achieves state-of-the-art performance compared to existing
methods.
