transformer trajectory prediction

October 6, 2023

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. PDF Trajformer: Trajectory Prediction with Local Self-Attentive Contexts ... Trajectory forecasting is the task of predicting future objects (or people's) motion given past trajectories. vanilla transformer to model the trajectory sequences. TrAISformer-A generative transformer for AIS trajectory prediction Essentially, this convert the RL problem into a . PDF S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction ... Knowledge Graph-Based Enhanced Transformer for Metro ... - Hindawi We propose a Transformer model to predict destinations from partial trajectories and we demonstrate its use on two datasets from different domains, including a simulated indoor dataset and an outdoor taxi trajectory dataset. PDF Multimodal Motion Prediction With Stacked Transformers 1 Introduction Pedestrian trajectory prediction anticipates the future bound- ing boxes of pedestrians in an ego-centric view of a mov- ing vehicle, which is critical for autonomous driving sys- tems to avoid possible collisions. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. 2021自动驾驶论文总览（Cvpr+Icra+Iros） - 知乎 PDF Transformer-based Long-Term Viewport Prediction in 360 Video: Scanpath ... PDF Modern Approach for Multi Object Tracking and Trajectory Prediction Trajectory prediction of road participants like vehicles and pedestrians is of great signiﬁcance for planning and decision making of autonomous vehicles. Kris Kitani. PDF Learning Generative Vision Transformer with Energy-Based Latent Space ... In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms. Sequence Modeling Solutions - The Berkeley Artificial Intelligence ... Modelling trajectory in general, and vessel trajectory in particular, is a difficult . Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory ... We question the use of the LSTM models and propose the novel use of Transformer Networks for trajectory forecasting. First time is the original transformer prediction model, second time is our GNN encoder model. applied to many time series prediction problems such as pedestrian trajectory prediction [1, 36] and trafﬁc prediction [34]. In this work, we present an attention-based framework for data-driven operator learning, which we term Operator Transformer (OFormer).