  Traditional approaches to prediction of future trajectory of road agents rely
on knowing information about their past trajectory. This work rather relies
only on having knowledge of the current state and intended direction to make
predictions for multiple vehicles at intersections. Furthermore, message
passing of this information between the vehicles provides each one of them a
more holistic overview of the environment allowing for a more informed
prediction. This is done by training a neural network which takes the state and
intent of the multiple vehicles to predict their future trajectory. Using the
intention as an input allows our approach to be extended to additionally
control the multiple vehicles to drive towards desired paths. Experimental
results demonstrate the robustness of our approach both in terms of trajectory
prediction and vehicle control at intersections. The complete training and
evaluation code for this work is available here:
\url{https://github.com/Dekai21/Multi_Agent_Intersection}.
