TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction

Autor:	Wang, Yuting, Zhou, Hangning, Zhang, Zhigang, Feng, Chen, Lin, Huadong, Gao, Chaofei, Tang, Yizhi, Zhao, Zhenting, Zhang, Shiyu, Guo, Jie, Wang, Xuefeng, Xu, Ziyao, Zhang, Chi
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.00170 Zobrazit plný text záznamu View this record from Arxiv