Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for the safe and efficient operation of connected automated vehicles under complex driving situations in the real world. The multi-agent prediction task is challenging, as the motions of traffic participants are affected by many factors, including their individual dynamics, their interactions with surrounding agents, the traffic infrastructures, and the number and modalities of the target agents. To further advance the trajectory prediction techniques, in this work we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT), which is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, the agent's dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph, and then interaction features are extracted using the proposed HEAT network. Besides, the map features are shared across all agents by introducing a selective gate mechanism. And finally, the trajectories of multi-agent are executed simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy, demonstrating its feasibility and effectiveness.

翻译：对多种不同交通参与者同时进行轨迹预测,对于在现实世界中复杂驾驶情况下相关自动车辆安全高效运行至关重要。多试剂预测任务具有挑战性,因为交通参与者的动作受到许多因素的影响,包括他们的个人动态、他们与周围物剂的相互作用、交通基础设施以及目标物剂的数量和模式。为了进一步推进轨迹预测技术,我们在这项工作中提议了一个三道框架和一个新型的异质强化电磁图搜索网(HEAT),它能够处理目标物剂和所涉交通参与者的异质。具体地说,该物剂的动态是从其历史状态中提取的,这些因素包括他们的个人动态、他们与周围物剂的互动、交通基础设施以及目标物剂的数量和方式。为了进一步推进轨迹预测技术,我们在此工作中提议了一个三道框架以及一个新型的异质增强型电磁图(HEAT)。最后,多试剂的轨迹是同时执行的。使用城市和高速公路驱动数据装置和所涉交通参与者的异质。具体地从他们的历史状态中提取的动态数据,表明其模型的准确性能。