MissFormer: (In-)Attention-Based Handling of Missing Observations for ... the trajectory direction of the green pedestrian is straight forward, and that of the red pedestrian deflects to avoid the collision with the green pedestrian. Multimodal Motion Prediction Framework Motion prediction aims to accurately predict the future This task is challenging for autonomous driving since agents (e.g., vehicles and pedestrians) and their associated behaviors may be diverse and influence one another. Code for "Transformer Networks for Trajectory Forecasting". Modelling trajectory in general, and vessel trajectory in particular, is a difficult . Keywords: trajectory prediction, motion forecasting, multi-task learning, attention, autonomous vehicles; Abstract: Predicting the motion of multiple agents is necessary for planning in dynamic environments. Towards this end, this paper introduces a transformer-based approach for handling missing observations in variable input length trajectory data. Trajectory Transformer Overview The Trajectory Transformer model was proposed in Offline Reinforcement Learning as One Big Sequence Modeling Problem by Michael Janner, Qiyang Li, Sergey Levine.. Traditional trajectory prediction methods mostly use machine learning methods, such as the hidden Markov model , mixed hidden Markov model , Bayesian inference , and Gaussian mixture model . As with the Decision Transformer, the Trajectory Transformer uses a GPT as its backbone, and is trained to optimize log probabilities of states, actions, and rewards, conditioned on prior information in the trajectory. 2 4thyear PhD candidate (2018-) at . 10.1109/3dv53792.2021.00066 Our model performs hand and object interaction reasoning via the self-attention mechanism in Transformers. Our key observation is that a human's action and behaviors may highly depend on the other persons around. Kris Kitani. We then use this data to train an Object-Centric Transformer (OCT) model for prediction. Multi-scale graph-transformer network for trajectory prediction of the ... works in the trajectory domain while simultaneously drawing global attention among di erent joints, as well as between the input historical trajectories and the output predictions. First time is the original transformer prediction model, second time is our GNN encoder model.