Results 311 to 320 of about 2,718,700 (352)
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ACM Transactions on Spatial Algorithms and Systems, 2015
Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering “semantics” from the raw geometric trajectories by relating them to the spatial ...
R. H. Guting, F. Valdes, M.L. Damiani
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Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering “semantics” from the raw geometric trajectories by relating them to the spatial ...
R. H. Guting, F. Valdes, M.L. Damiani
openaire +1 more source
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
European Conference on Computer Vision, 2020Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots.
K. Mangalam +6 more
semanticscholar +1 more source
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
European Conference on Computer Vision, 2020Understanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. This is challenging because it requires effectively modeling the socially aware crowd spatial interaction and complex temporal ...
Cunjun Yu +4 more
semanticscholar +1 more source
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
IEEE Intelligent Transportation Systems Magazine, 2022Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system applications ranging from traffic simulations to driver assistance.
Lei Lin +3 more
semanticscholar +1 more source
Journal of Adolescence, 2013
AbstractThis special section brings together five studies using group‐based modeling to capture developmental trajectories of loneliness from age 7 through age 20. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood at a continuum but reflect distinct ...
Manfred H M, van Dulmen, Luc, Goossens
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AbstractThis special section brings together five studies using group‐based modeling to capture developmental trajectories of loneliness from age 7 through age 20. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood at a continuum but reflect distinct ...
Manfred H M, van Dulmen, Luc, Goossens
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RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
International Conference on Learning Representations, 2023Generalization remains one of the most important desiderata for robust robot learning systems. While recently proposed approaches show promise in generalization to novel objects, semantic concepts, or visual distribution shifts, generalization to new ...
Jiayuan Gu +16 more
semanticscholar +1 more source
Trajectory Unified Transformer for Pedestrian Trajectory Prediction
IEEE International Conference on Computer Vision, 2023Pedestrian trajectory prediction is an essential link to understanding human behavior. Recent work achieves state-of-the-art performance gained from hand-designed post-processing, e.g., clustering.
Liushuai Shi +3 more
semanticscholar +1 more source
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
IEEE International Conference on Computer Vision, 2019Human trajectory prediction is challenging and critical in various applications (e.g., autonomous vehicles and social robots). Because of the continuity and foresight of the pedestrian movements, the moving pedestrians in crowded spaces will consider ...
Yingfan Huang +4 more
semanticscholar +1 more source
Applied Mathematics and Computation, 2001
The following theorem is proved by using a continuation method: Consider an autonomous differential equation system \[ x'(t)= f(x(t)),\quad x(t_0)= x_0,\tag{1} \] where \(f: \mathbb{R}^n\to \mathbb{R}^n\) satisfies 1. \(f\in C^1(\mathbb{R}^n)\); 2. There is a constant \(k\) such that \(\| f(x)\|< k\) when \(x(\cdot)\) is a solution to problem (1); 3 ...
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The following theorem is proved by using a continuation method: Consider an autonomous differential equation system \[ x'(t)= f(x(t)),\quad x(t_0)= x_0,\tag{1} \] where \(f: \mathbb{R}^n\to \mathbb{R}^n\) satisfies 1. \(f\in C^1(\mathbb{R}^n)\); 2. There is a constant \(k\) such that \(\| f(x)\|< k\) when \(x(\cdot)\) is a solution to problem (1); 3 ...
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