​​I'm happy to announce that our team (me, Stepan Konev, Kirill Brodt) was awarded🏅 3rd place within the Waymo Motion Prediction Challenge 2021.

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 from the research community. We present a simple and yet very strong baseline for multimodal motion prediction based purely on Convolutional Neural Networks.

The task is the following: Given agents' tracks for the past 1 second on a corresponding map, we had to predict the positions of the agents on the road for 8 seconds into the future.

Our model takes a raster image centered around a target agent as input and directly predicts a set of possible trajectories along with their confidences. The raster image is obtained by rasterisation of a scene and the history of all the agents. While being easy-to-implement, the proposed approach achieves competitive performance compared to the state-of-the-art methods on the Waymo Open Dataset Motion Prediction Challenge (2021): Our model ranks 1st using minimum average displacement error and 3rd using mAP score.

We wrote a small paper and release our code!

📜Technical report
⚒Code