  Self-driving vehicles (SDVs) hold great potential for improving traffic
safety and are poised to positively affect the quality of life of millions of
people. To unlock this potential one of the critical aspects of the autonomous
technology is understanding and predicting future movement of vehicles
surrounding the SDV. This work presents a deep-learning-based method for
kinematically feasible motion prediction of such traffic actors. Previous work
did not explicitly encode vehicle kinematics and instead relied on the models
to learn the constraints directly from the data, potentially resulting in
kinematically infeasible, suboptimal trajectory predictions. To address this
issue we propose a method that seamlessly combines ideas from the AI with
physically grounded vehicle motion models. In this way we employ best of the
both worlds, coupling powerful learning models with strong feasibility
guarantees for their outputs. The proposed approach is general, being
applicable to any type of learning method. Extensive experiments using deep
convnets on real-world data strongly indicate its benefits, outperforming the
existing state-of-the-art.
