  To plan a safe and efficient route, an autonomous vehicle should anticipate
future motions of other agents around it. Motion prediction is an extremely
challenging task that recently gained significant attention within the research
community. In this work, we present a simple and yet very strong baseline for
multimodal motion prediction based purely on Convolutional Neural Networks.
While being easy-to-implement, the proposed approach achieves competitive
performance compared to the state-of-the-art methods and ranks 3rd on the 2021
Waymo Open Dataset Motion Prediction Challenge. Our source code is publicly
available at GitHub
