{"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/crat-pred-vehicle-trajectory-prediction-with","title":"CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention","arxiv_id":"2202.04488","date":"2022-02-09","proceeding":null,"authors":["Julian Schmidt","Julian Jordan","Franz Gritschneder","Klaus Dietmayer"],"abstract":"Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily rely on map information. In reality, however, this information is not always available. We therefore propose CRAT-Pred, a multi-modal and non-rasterization-based trajectory prediction model, specifically designed to effectively model social interactions between vehicles, without relying on map information. CRAT-Pred applies a graph convolution method originating from the field of material science to vehicle prediction, allowing to efficiently leverage edge features, and combines it with multi-head self-attention. Compared to other map-free approaches, the model achieves state-of-the-art performance with a significantly lower number of model parameters. In addition to that, we quantitatively show that the self-attention mechanism is able to learn social interactions between vehicles, with the weights representing a measurable interaction score. The source code is publicly available.","url_abs":"https://arxiv.org/abs/2202.04488v2","url_pdf":"https://arxiv.org/pdf/2202.04488v2.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":"crat-pred-vehicle-trajectory-prediction-with","repo_url":"https://github.com/schmidt-ju/crat-pred","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"motion-forecasting","task_name":"Motion Forecasting"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-forecasting-on-argoverse-cvpr-2020","task":"Motion Forecasting","dataset":"Argoverse CVPR 2020","model":"CRAT-Pred","rank_in_archive_order":174,"of":299,"metrics":{"DAC (K=6)":"0.9558","MR (K=1)":"0.6323","MR (K=6)":"0.2624","brier-minFDE (K=6)":"2.5926","minADE (K=1)":"1.8162","minADE (K=6)":"1.0626","minFDE (K=1)":"4.0576","minFDE (K=6)":"1.8981"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}