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Traditional approaches to prediction of future trajectory of road agents relyon knowing information about their past trajectory. This work rather reliesonly on having knowledge of the current state and intended direction to makepredictions for multiple vehicles at intersections. Furthermore, messagepassing of this information between the vehicles provides each one of them amore holistic overview of the environment allowing for a more informedprediction. This is done by training a neural network which takes the state andintent of the multiple vehicles to predict their future trajectory. Using theintention as an input allows our approach to be extended to additionallycontrol the multiple vehicles to drive towards desired paths. Experimentalresults demonstrate the robustness of our approach both in terms of trajectoryprediction and vehicle control at intersections. The complete training andevaluation code for this work is available here:\url{https://github.com/Dekai21/Multi_Agent_Intersection}.

Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information