An effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (e.g. autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios. Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent. In this paper, we propose a generic generative neural system (called STG-DAT) for multi-agent trajectory prediction involving heterogeneous agents. The system takes a step forward to explicit interaction modeling by incorporating relational inductive biases with a dynamic graph representation and leverages both trajectory and scene context information. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The constraint not only ensures physical feasibility but also enhances model performance. Moreover, the proposed prediction model can be easily adopted by multi-target tracking frameworks. The tracking accuracy proves to be improved by empirical results. The proposed system is evaluated on three public benchmark datasets for trajectory prediction, where the agents cover pedestrians, cyclists and on-road vehicles. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction and tracking accuracy.

中文翻译：

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时空图双关注网络的多智能体预测与跟踪

当智能移动系统（例如自动驾驶汽车和社交机器人）在高度交互和拥挤的场景中导航时，对环境的有效了解和对周围动态障碍物的准确轨迹预测对于实现安全，高质量的计划是必不可少的。由于场景演变中存在频繁的交互作用和不确定性，因此希望预测系统能够对不同实体进行关系推理，并为每个主体提供未来轨迹的分布。在本文中，我们提出了一个通用的生成神经系统（称为STG-DAT），用于涉及异构代理的多代理轨迹预测。该系统通过将关系归纳偏差与动态图形表示相结合，向显式交互建模迈出了一步，并利用了轨迹和场景上下文信息。我们还采用了应用于车辆轨迹预测的有效运动学约束层。该约束不仅可以确保物理可行性，而且可以提高模型性能。而且，所提出的预测模型可以容易地被多目标跟踪框架采用。经验结果证明了跟踪精度的提高。所提议的系统在三个用于轨迹预测的公共基准数据集上进行了评估，其中代理商涵盖了行人，骑自行车的人和公路车辆。