Dynamical complex systems composed of interactive heterogeneous agents are prevalent in the world, including urban traffic systems and social networks. Modeling the interactions among agents is the key to understanding and predicting the dynamics of the complex system, e.g., predicting the trajectories of traffic participants in the city. Compared with interaction modeling in homogeneous systems such as pedestrians in a crowded scene, heterogeneous interaction modeling is less explored. Worse still, the error accumulation problem becomes more severe since the interactions are more complex. To tackle the two problems, this article proposes heterogeneous interaction modeling with reduced accumulated error (HIMRAE) for multiagent trajectory prediction. Based on the historical trajectories, our method infers the dynamic interaction graphs among agents, featured by directed interacting relations and interacting effects. A heterogeneous attention mechanism (HAM) is defined on the interaction graphs for aggregating the influence from heterogeneous neighbors to the target agent. To alleviate the error accumulation problem, this article analyzes the error sources from the spatial and temporal perspectives, and proposes to introduce the graph entropy and the mixup training strategy for reducing the two types of errors, respectively. Our method is examined on three real-world datasets containing heterogeneous agents, and the experimental results validate the superiority of our method.

中文翻译：

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用于多代理轨迹预测的减少累积误差的异构交互建模。

由交互式异构代理组成的动态复杂系统在世界上很普遍，包括城市交通系统和社交网络。对代理之间的交互进行建模是理解和预测复杂系统动态的关键，例如，预测城市中交通参与者的轨迹。与拥挤场景中的行人等同质系统中的交互建模相比，异构交互建模的探索较少。更糟糕的是，由于交互更加复杂，错误累积问题变得更加严重。为了解决这两个问题，本文提出了用于多智能体轨迹预测的具有减少累积误差的异构交互建模 (HIMRAE)。基于历史轨迹，我们的方法推断出代理之间的动态交互图，具有定向相互作用关系和相互作用效应的特点。在交互图上定义了异构注意机制（HAM），用于聚合异构邻居对目标代理的影响。为了缓解误差累积问题，本文从空间和时间的角度分析误差来源，并提出分别引入图熵和混合训练策略来减少两类误差。我们的方法在三个包含异构代理的真实数据集上进行了检查，实验结果验证了我们方法的优越性。在交互图上定义了异构注意机制（HAM），用于聚合异构邻居对目标代理的影响。为了缓解误差累积问题，本文从空间和时间的角度分析误差来源，并提出分别引入图熵和混合训练策略来减少两类误差。我们的方法在三个包含异构代理的真实数据集上进行了检查，实验结果验证了我们方法的优越性。在交互图上定义了异构注意机制（HAM），用于聚合异构邻居对目标代理的影响。为了缓解误差累积问题，本文从空间和时间的角度分析误差来源，并提出分别引入图熵和混合训练策略来减少两类误差。我们的方法在三个包含异构代理的真实数据集上进行了检查，实验结果验证了我们方法的优越性。