MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
This work provides a strong, easy-to-implement baseline for motion prediction in autonomous driving, which is an incremental improvement over existing methods.
The authors tackled the problem of multimodal motion prediction for autonomous driving by proposing a simple convolutional neural network baseline, achieving competitive performance and ranking 3rd on the 2021 Waymo Open Dataset Motion Prediction Challenge.
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