  Effective understanding of the environment and accurate trajectory prediction
of surrounding dynamic obstacles are indispensable for intelligent mobile
systems (like 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 Social-WaGDAT) for multi-agent trajectory prediction,
which makes 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 which not
only ensures physical feasibility but also enhances model performance. 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 accuracy.
