公开(公告)号：US11485387B2

公开(公告)日：2022-11-01

申请号：US17114977

申请日：2020-12-08

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Michael D. Howard ,  Hyukseong Kwon ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  B60W50/00 ,  B60W30/095 ,  B60W40/06 ,  G01C21/34 ,  B60W40/09 ,  G01C21/36 ,  G06N5/02

Abstract: A method of predictive navigation control for an ego vehicle includes: 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 representing distances, speeds and headings of one or more newly observed objects about the ego vehicle, and wherein the episodic memory structure includes a network of nodes each representing a respective previously existing event and having a respective node risk and likelihood; determining which of the nodes has a smallest respective difference metric, thus defining a best matching node; consolidating the cue node with the best matching node if the smallest difference metric is less than a match tolerance, else adding a new node corresponding to the cue node to the episodic memory structure; and identifying a likeliest next node and/or a riskiest next node.

公开(公告)号：US20220177002A1

公开(公告)日：2022-06-09

申请号：US17114977

申请日：2020-12-08

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Michael D. Howard ,  Hyukseong Kwon ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  G06N5/02 ,  B60W50/00 ,  B60W30/095 ,  B60W40/06 ,  G01C21/34 ,  B60W40/09 ,  G01C21/36

Abstract: A method of predictive navigation control for an ego vehicle includes: 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 representing distances, speeds and headings of one or more newly observed objects about the ego vehicle, and wherein the episodic memory structure includes a network of nodes each representing a respective previously existing event and having a respective node risk and likelihood; determining which of the nodes has a smallest respective difference metric, thus defining a best matching node; consolidating the cue node with the best matching node if the smallest difference metric is less than a match tolerance, else adding a new node corresponding to the cue node to the episodic memory structure; and identifying a likeliest next node and/or a riskiest next node.