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MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
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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
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Cited by 1 Pith paper
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TrajFlow: Multi-modal Motion Prediction via Flow Matching
TrajFlow uses flow matching with a multi-query transformer to predict multiple trajectories in one pass and a Plackett-Luce ranking loss to improve confidence scores, reporting small SOTA gains on WOMD.
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