System and method for predictive navigation control

A method of optimizing a decision for a decider, comprising:

• comparing a cue node to each of a plurality of episodic memory nodes in an episodic memory structure, wherein the cue node represents a new event associated with a set of respective attributes associated with one or more newly observed stimuli each assigned to a respective one of a plurality of newly defined attention zones defined with respect to a current state of the decider, and wherein the episodic memory structure comprises a selectively interconnected and directed network of the episodic memory nodes, wherein each episodic memory node represents a respective previously existing event, with each previously existing event being associated with a respective set of attributes associated with one or more previously observed stimuli each assigned to a respective one of a plurality of previously defined attention zones defined with respect to a respective previous state of the decider, and wherein each episodic memory node has a respective node risk and a respective likelihood associated therewith;
• determining which of the plurality of episodic memory nodes has a smallest respective difference metric, thus defining a best matching episodic memory node, wherein each respective difference metric is determined based on a respective aggregate difference between one or more respective aspects of the respective episodic memory node and the cue node;
• consolidating the cue node with the best matching episodic memory node if the smallest respective difference metric is less than a predetermined match tolerance, or adding a new episodic memory node corresponding to the cue node to the episodic memory structure if the smallest respective difference metric is greater than or equal to the predetermined match tolerance;
• and
• identifying one or both of (i) a likeliest next episodic memory node among one or more episodic memory nodes immediately downstream from the best matching or new episodic memory node, wherein the likeliest next episodic memory node has a highest likelihood among the immediately downstream episodic memory nodes, and (ii) a riskiest next episodic memory node among the one or more episodic memory nodes immediately downstream from the best matching or new episodic memory node, wherein the riskiest next episodic memory node has a highest node risk among the immediately downstream episodic memory nodes.

A controller for predictive navigation control for an ego vehicle, comprising:

• (a) a data ingest module configured to receive inputs relating to a cue node, wherein the cue node represents a new event associated with a set of respective locations, speeds and headings associated with one or more newly observed objects each located within a respective one of a plurality of newly defined attention zones about the ego vehicle, the data ingest module being further configured to:
• calculate a respective individual risk for each of the one or more newly observed objects using a sigmoidal function applied to a respective distance between each respective newly observed object and the ego vehicle;
• establish a respective overall risk for each of the plurality of newly defined attention zones, based on the respective individual risks of the one or more newly observed objects located within each respective newly defined attention zone;
• and
• define the cue node as a grouping of the plurality of newly defined attention zones organized according to their respective overall risks;
• (b) an episodic memory module operatively connected with the data ingest module and containing an episodic memory structure comprising a selectively interconnected and directed network of episodic memory nodes, wherein each episodic memory node represents a respective previously existing event, with each previously existing event being associated with a respective set of locations, speeds and headings associated with one or more previously observed objects each located within a respective one of a plurality of previously defined attention zones about the ego vehicle, and wherein each episodic memory node has a respective node risk and a respective likelihood associated therewith, the episodic memory module being configured to:
• compare the cue node to each of the episodic memory nodes;
• determine which of the episodic memory nodes has a smallest respective difference metric, thus defining a best matching episodic memory node, wherein each respective difference metric is determined based on a respective aggregate difference between one or more respective characteristics of the respective episodic memory node and the cue node;
• and
• consolidate the cue node with the best matching episodic memory node if the smallest respective difference metric is less than a predetermined match tolerance, or add a new episodic memory node corresponding to the cue node to the episodic memory structure if the smallest respective difference metric is greater than or equal to the predetermined match tolerance;
• and
• (c) a prediction module operatively connected with the episodic memory module and configured to identify one or both of (i) a likeliest next episodic memory node among one or more episodic memory nodes immediately downstream from the best matching or new episodic memory node, wherein the likeliest next episodic memory node has a highest likelihood among the immediately downstream episodic memory nodes, and (ii) a riskiest next episodic memory node among the one or more episodic memory nodes immediately downstream from the best matching or new episodic memory node, wherein the riskiest next episodic memory node has a highest node risk among the immediately downstream episodic memory nodes.