In practice, the prediction of aircraft trajectories needs to consider the impact of various sources, such as environmental conditions, pilot/controller behaviors, and potential conflicts with nearby aircraft. Firstly, we utilize stacked transformers architecture to incoporate multiple channels of contextual information, and model the multimodality at feature level with a set of trajectory proposals. Abstract: We propose a novel framework for multi-person 3D motion trajectory prediction. Our key observation is that a human's action and behaviors may highly depend on the other persons around. STAR models intra-graph crowd interaction by TGConv, a novel Transformer-based graph convolution mechanism. (PDF) Transformer based trajectory prediction The model is formed indirectly by successively increasing the complexity of the demanded inference tasks. Predicting motion of surrounding agents is critical to real-world applications of tactical path planning for autonomous driving. PDF Transformer-based Long-Term Viewport Prediction in 360 Video: Scanpath ... This work presents a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has shown its effectiveness in conditions of domain change. of destination prediction in a contextless data setting where we solely learn from trajectory coordinate information. This enables test-time prediction by sampling from the trained model using what is known as beam search. Multimodal Motion Prediction with Stacked Transformers Transformer Networks for Trajectory Forecasting STAR models intra-graph crowd interaction by TGConv, a novel Transformer-based graph convolution mechanism. ICCV 2021 •Tokens are permutation-invariant in self-attention (no order of information) . Transformer has demonstrated outstanding performance in dealing with sequential data. This is a fundamental switch from the sequential step-by-step processing of LSTMs to the only-attention-based memory mechanisms of Transformers. 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. Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. For pedestrian trajectory prediction, the number of pedestrians in one frame is in the scale of about hundred. These are "simple" model because each person is modelled separately without any complex human-human nor scene interaction terms.
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