  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.
