{"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/mixnet-structured-deep-neural-motion","title":"MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing","arxiv_id":"2208.01862","date":"2022-08-03","proceeding":null,"authors":["Phillip Karle","Ferenc Török","Maximilian Geisslinger","Markus Lienkamp"],"abstract":"Reliably predicting the motion of contestant vehicles surrounding an autonomous racecar is crucial for effective and performant planning. Although highly expressive, deep neural networks are black-box models, making their usage challenging in safety-critical applications, such as autonomous driving. In this paper, we introduce a structured way of forecasting the movement of opposing racecars with deep neural networks. The resulting set of possible output trajectories is constrained. Hence quality guarantees about the prediction can be given. We report the performance of the model by evaluating it together with an LSTM-based encoder-decoder architecture on data acquired from high-fidelity Hardware-in-the-Loop simulations. The proposed approach outperforms the baseline regarding the prediction accuracy but still fulfills the quality guarantees. Thus, a robust real-world application of the model is proven. The presented model was deployed on the racecar of the Technical University of Munich for the Indy Autonomous Challenge 2021. The code used in this research is available as open-source software at www.github.com/TUMFTM/MixNet.","url_abs":"https://arxiv.org/abs/2208.01862v1","url_pdf":"https://arxiv.org/pdf/2208.01862v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"mixnet-structured-deep-neural-motion","repo_url":"https://github.com/tumftm/mixnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}