Gaussian Process for Trajectories
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories. A Gaussian process models measurements of a trajectory as coming from a multidimensional Gaussian, and it produces for each timestamp a Gaussian distribution as a prediction. We discuss elements that need to be considered when applying Gaussian process to trajectories, common choices for those elements, and provide a concrete example of implementing a Gaussian process.
Code (0)
등록된 구현이 없습니다.
Tasks
Gaussian ProcessesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems
This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these traj…
Gaussian ProcessesregressionGaussian behaviors: representations and data-driven control
We propose a modeling framework for stochastic systems based on Gaussian processes. Finite-length trajectories of the system are modeled as random vectors from a Gaussian distribution, which we call a Gaussian behavior. …
Gaussian ProcessesState Space ModelsLearning Probabilistic Intersection Traffic Models for Trajectory Prediction
Autonomous agents must be able to safely interact with other vehicles to integrate into urban environments. The safety of these agents is dependent on their ability to predict collisions with other vehicles' future traje…
Collision AvoidanceObject RecognitionPredictionTime Series Analysis+1The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models
Computing the distribution of trajectories from a Gaussian Process model of a dynamical system is an important challenge in utilizing such models. Motivated by the computational cost of sampling-based approaches, we cons…
Gaussian ProcessesState Space ModelsLearning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Hamiltonian mechanics is one of the cornerstones of natural sciences. Recently there has been significant interest in learning Hamiltonian systems in a free-form way directly from trajectory data. Previous methods have t…
Gaussian Processes