{"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/tenet-transformer-encoding-network-for","title":"TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction","arxiv_id":"2207.00170","date":"2022-06-30","proceeding":null,"authors":["Yuting Wang","Hangning Zhou","Zhigang Zhang","Chen Feng","Huadong Lin","Chaofei Gao","Yizhi Tang","Zhenting Zhao","Shiyu Zhang","Jie Guo","Xuefeng Wang","Ziyao Xu","Chi Zhang"],"abstract":"This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction. Besides, we propose the Temporal Flow Header to enhance the trajectory encoding. In the end, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.","url_abs":"https://arxiv.org/abs/2207.00170v1","url_pdf":"https://arxiv.org/pdf/2207.00170v1.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"motion-forecasting","task_name":"Motion Forecasting"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-forecasting-on-argoverse-cvpr-2020","task":"Motion Forecasting","dataset":"Argoverse CVPR 2020","model":"MacFormer","rank_in_archive_order":12,"of":299,"metrics":{"DAC (K=6)":"0.9863","MR (K=1)":"0.5596","MR (K=6)":"0.1272","brier-minFDE (K=6)":"1.7667","minADE (K=1)":"1.6565","minADE (K=6)":"0.8121","minFDE (K=1)":"3.6081","minFDE (K=6)":"1.2141"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.00170","atlas_url":"https://app.syntology.ai/?focus=2207.00170","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}