In this paper, we introduce the Spatio-Temporal grAph tRansformer (STAR) framework, a novel framework for spatio-temporal trajectory prediction based purely on self-attention mechanism. These are "simple" model because each person is modelled separately without any complex human-human nor scene interaction terms. We evaluate VPT360 over three widely-used . 3.1 Overview In this section, we introduce the proposed spatio-temporal graph Transformer based trajectory prediction framework, STAR. Then, we induce the multimodality via a . 1 HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent ... For pedestrian trajectory prediction, the number of pedestrians in one frame is in the scale of about hundred. Our model performs hand and object interaction reasoning via the self-attention mechanism in Transformers. Multi-Person 3D Motion Prediction with Multi-Range Transformers To predict future trajectories, interactions between surrounding traffic are needed to be modelled. In particular, we consider both the original Transformer Network (TF) and the larger . This task is challenging for several reasons: in fact, the future motion depends on interactions among objects and interactions of the . PDF S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction ... Meanwhile, a Transformer encoder is applied in our method to extract the temporal information from the fused feature sequence. In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms. 2021自动驾驶论文总览（Cvpr+Icra+Iros） - 知乎 These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles. vanilla transformer to model the trajectory sequences. Trajectory forecasting with Transformer networks - Addfor S.p.A ... Taghavi et al. In the concurrent work Scene Transformer[22], they used Transformer for both spatial and temporal dimension, adopted mask strategies and done . Its . Transformer for Multi-Agent Trajectory Prediction via Scene Encoding Xiaosong Jia, Penghao Wu, Li Chen, Hongyang Li, Yu Liu, Junchi Yan, Senior Member, IEEE Abstract—One essential task for autonomous driving is to encode the information of a driving scene into vector representations so
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