Results 301 to 310 of about 27,601,135 (361)

Spatial Lanchester models

European Journal of Operational Research, 2011
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Eduardo González, Marcelo J. Villena
openaire   +1 more source

Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis.

Accident Analysis and Prevention, 2018
According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle ...
M. Hosseinpour, Sina Sahebi, Z. H. Zamzuri, A. Yahaya, N. Ismail   +4 more
semanticscholar   +1 more source

Large Spatial Model: End-to-end Unposed Images to Semantic 3D

Neural Information Processing Systems
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into multiple subtasks, each requiring complex transformations between different ...
Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen, Shijie Zhou, Achuta Kadambi, Zhangyang Wang, Danfei Xu, B. Ivanovic, Marco Pavone, Yue Wang   +12 more
semanticscholar   +1 more source

Modeling with spatial patches

Proceedings SMI. Shape Modeling International 2002, 2003
Spatial patches have been introduced (Ivanov and Kuzmin, 2001) as an alternative primitive for the representation of 3D objects potentially acquired from real life. Defined as range images of small regions of an object's surface, they have proven to allow for efficient storage and rendering of such surfaces, and they also exhibit great descriptive ...
Denis V. Ivanov, Yevgeniy Kuzmin, Victor S. Lempitsky   +2 more
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SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

Robotics
In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundation model.
Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yani Ding, Zhigang Wang, Jiayuan Gu, Bin Zhao, Dong Wang, Xuelong Li   +10 more
semanticscholar   +1 more source

Model Misspecification and Spatial Structure in Spatial-Interaction Models

Environment and Planning A: Economy and Space, 1983
Concern has been expressed about the effects of spatial structure on parameter estimates from spatial-interaction models. The problem is essentially one of model misspecification. With a correctly specified model assumed, in which destination attraction depends on whether it is near to an origin or not, the consequences of using a misspecified model ...
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SpatialRGPT: Grounded Spatial Reasoning in Vision Language Model

Neural Information Processing Systems
Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited.
An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, Sifei Liu   +7 more
semanticscholar   +1 more source

Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting

IEEE Transactions on Knowledge and Data Engineering, 2021
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of ...
Shengna Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, G. Cong   +4 more
semanticscholar   +1 more source

Modelling Spatial Structures

2015
Data is spatial if it contains references to space. We can easily detect explicit references, for example coordinates, but we cannot detect whether data implicitly contains references to space, and whether it has properties of spatial data, if additional semantic information is missing.
Franz-Benjamin Mocnik, Andrew U. Frank
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Spatial Evolutionary Modeling

2001
Evolutionary models (e.g., genetic algorithms, artificial life), explored in other fields for the past two decades, are now emerging as an important new tool in GIS for a number of reasons. First, they are highly appropriate for modeling geographic phenomena.
Roman M. Krzanowski, Jonathan Raper
openaire   +1 more source