11. A computer-implemented method for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, comprising: an act of causing a data processor to execute instructions stored on a non-transitory memory such that upon execution, one or more processors perform operations of: using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network; comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle; using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match; if a match is identified, modifying the adaptable predictive model that is the match according to a second set of sensory data, and controlling the vehicle using control outputs generated using the match; and if a match is not identified, generating a new adaptable predictive model through automatic model learning using the second set of sensory data, and controlling the vehicle using control outputs generated using the new adaptable predictive model.
11. A computer-implemented method for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, comprising: an act of causing a data processor to execute instructions stored on a non-transitory memory such that upon execution, one or more processors perform operations of: using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network; comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle; using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match; if a match is identified, modifying the adaptable predictive model that is the match according to a second set of sensory data, and controlling the vehicle using control outputs generated using the match; and if a match is not identified, generating a new adaptable predictive model through automatic model learning using the second set of sensory data, and controlling the vehicle using control outputs generated using the new adaptable predictive model. 16. The method as set forth in claim 11 , wherein the identification of the match in the set of competing adaptable predictive models is performed using a down-selection method that compares the set of competing adaptable predictive models over at least two time intervals of current sensory data, wherein the down-selection method ensures that when selecting a new adaptable predictive model, a selection is made only between those competing adaptable predictive models that are consistent with the history of sensory data.