{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/motioncnn-a-strong-baseline-for-motion","title":"MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving","arxiv_id":"2206.02163","date":"2022-06-05","proceeding":null,"authors":["Stepan Konev","Kirill Brodt","Artsiom Sanakoyeu"],"abstract":"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","url_abs":"https://arxiv.org/abs/2206.02163v1","url_pdf":"https://arxiv.org/pdf/2206.02163v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"motioncnn-a-strong-baseline-for-motion","repo_url":"https://github.com/stepankonev/MotionCNN-Waymo-Open-Motion-Dataset","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"motioncnn-a-strong-baseline-for-motion","repo_url":"https://github.com/kbrodt/waymo-motion-prediction-2021","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"motioncnn-a-strong-baseline-for-motion","repo_url":"https://github.com/kit-mrt/road-barlow-twins","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.02163","atlas_url":"https://app.syntology.ai/?focus=2206.02163","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}