SYSTEM AND METHOD FOR CONDITIONAL MARGINAL DISTRIBUTIONS AT FLEXIBLE EVALUATION HORIZONS

The methods and systems are directed to computational approaches for training and using machine learning algorithms to predict the conditional marginal distributions of the position of agents at flexible evaluation horizons and can enables more efficient path planning. These methods model agent movement by training a deep neural network to predict the position of an agent through time. A neural ordinary differential equation (neural ODE) that represents this neural network can be used to determine the log-likelihood of the agent's position as it moves in time.