Multi-granularity scenarios understanding network for trajectory prediction

T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction

Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in single scale, fail to capture the diverse two-dimensional patterns of trajectories in different spatial scales. In this paper, we propose TCONV whi...

Building a Multi-Granularity Sensor Network Using 2-dominating Sets

Granularity in Multi-Method

Multi-method planning is an approach to using a set of different planning methods to simultaneously achieve planner completeness, planning time efficiency, and plan length reduction. Although it has been shown that coordinating a set of methods in a coarse-grained, problem-by-problem manner has the potential for approaching this ideal, such an approach can waste a significant amount of time in ...

Multi-Granularity Noise for Curvilinear Grid LIC

A major problem of the existing curvilinear grid Line Integral Convolution (LIC) algorithm is that the resulting LIC textures may be distorted after being mapped onto the parametric surfaces, since a curvilinear grid usually consists of cells of di erent sizes. This paper proposes a way for solving the problem through using multi-granularity noise as the input image for LIC. A stochastic sampli...

Curved Trajectory Prediction Using a Self-Organizing Neural Network

Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimat...