Non-kinematic behavioral mapping

• US 8,538,675 B2
• Filed: 06/01/2010
• Issued: 09/17/2013
• Est. Priority Date: 06/01/2009
• Status: Active Grant

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Richardson, James J.
• Primary Examiner(s): MARC-COLEMAN, MARTHE Y
• Application Number:
  US12/791,169
• Publication Number:
  US 20100305858A1
• Time in Patent Office: 1,204 Days
• Field of Search: 701/301, 701/300, 340/944, 340/539.23, 340/435, 348/143, 348/149, 348/152, 348/169, 348/171, 348/155, 382/103, 382/228, 382/173, 382/107, 180/278, 706/52, 345/473
• US Class Current: 701/301
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A system and methodology/processes for non-kinematic/behavioral mapping to a local area abstraction (LAA) includes a technique for populating an LAA wherein human behavior or other non-strictly-kinematic motion may be present.

Claims

1. A system for predicting the path of a pedestrian comprising:

a local area abstraction (LAA) grid;

a pedestrian prediction logic (PPL) processor which receives a geographical map or image and operates on object data wherein human behavior, pedestrian behavior or other non-strictly-Kinematic motion may be present to populate the LAA grid; and

a first pedestrian occupancy map (POM) overlaying the LAA grid, said POM including a probability distribution of future pedestrian an occupancy within the POM.

2. The system of claim 1 wherein the PPL processor provides probabilistic object information to a pedestrian warning system (PWS).

3. The system of claim 2 wherein the probability distribution for an individual pedestrian is based upon one or more parameters related to a pedestrian or an actor being modeled, including but not limited to positional location, speed, direction and size, said PPL computes probabilistic values and stores the values in said first POM.

4. The system of claim 1 wherein said first POM is a first one of a plurality of POMs, each of said plurality of POMs having a probability distribution corresponding to an individual pedestrian or other non-strictly-Kinematic actor.

5. The system of claim 4 wherein a single individual selected-POM is identified by performing a look-up or other procedure to determine the best/correct POM representation for a specific pedestrian or actor.

6. The system of claim 5 wherein for each POM chosen to model a current or future probable location of a subject pedestrian or actor, the probability distribution of the individual POM is overlaid onto and added to an existing LAA.

7. The system of claim 6 wherein the LAA corresponds to an intersection occupancy map (IOM).

8. The system of claim 7 wherein the POM corresponds to one of:

a pin POM (PPOM);

or a cell POM (CPOM).

9. The system of claim 7 wherein the POM corresponds to a pin POM (PPOM) which assumes that the probabilities defined by the POM occur at some finite number of distinct points, each defined to be a specific distance and bearing from a reference position and a reference heading of the PPOM.

10. The system of claim 7 wherein the POM corresponds to a cell POM (CPOM) which assumes that the probabilities defined by the POM occur within some finite number of distinct cells.

11. The system of claim 7 wherein the POM is provided having a shape corresponding to one of:

a rectangular shape;

a square shape;

an oval shape;

a round shape;

a triangular shape;

a polygonal shape;

a convex polygonal shape;

a non-convex polygonal shape;

a non-polygonal shape;

a convex non-polygonal shape; and

a non-convex non-polygonal shape.