Curved trajectory prediction using a self-organizing neural network
Related Experiment Videos
1Department of Computer Science, University of North Carolina at Chapel Hill, USA. marshall@computer.org
Abstract:
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 estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.
Related Concept Videos
Real-World Applications of Space Curves
Orthogonal Trajectories
Curves Defined by Parametric Equations
Space Curves
Curvilinear Motion: Normal and Tangential Components
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
Curvilinear Motion: Rectangular Components
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...