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.

翻译：为在复杂交通环境中实现安全驾驶，自动驾驶车辆需准确预测周围异构交通参与者（如车辆、行人、自行车等）的未来轨迹。考虑到交互特性，人类驾驶员习惯于推断不同操作执行后的未来场景。为充分利用交互影响，本文提出一种自我规划引导的多图卷积网络（EPG-MGCN），通过同时利用历史轨迹信息与自我车辆的未来规划信息，预测异构智能体的轨迹。该网络首先采用四种图拓扑结构（距离图、可见性图、规划图与类别图）建模社交交互；随后通过规划图及其后续的规划引导预测模块编码自我车辆的规划信息，以降低轨迹预测的不确定性；最后设计类别特定的门控循环单元（CS-GRU）编码器-解码器，为各类智能体生成其未来轨迹。我们在ApolloScape与NGSIM两个真实轨迹数据集上评估网络性能，实验结果表明，EPG-MGCN相较于现有方法取得了最先进的性能。