A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction

Temporal patterns (how vehicles behave in our observed past) underline our reasoning of how people drive on the road, and can explain why we make certain predictions about interactions among road agents. In this paper we propose the ConceptNet trajectory predictor - a novel prediction framework that is able to incorporate agent interactions as explicit edges in a temporal knowledge graph. We demonstrate the sample efficiency and the overall accuracy of the proposed approach, and show that using the graphical structure to explicitly model interactions enables better detection of agent interactions and improved trajectory predictions on a large real-world driving dataset.