Method and apparatus for providing goal oriented navigational directions

• US 10,503,988 B2
• Filed: 08/10/2017
• Issued: 12/10/2019
• Est. Priority Date: 08/10/2016
• Status: Active Grant

• Current Assignee: Xevo, Inc. (Lear Corporation)
• Original Assignee: Xevo, Inc. (Lear Corporation)
• Inventors: Cordell, John Palmer, Welland, Robert Victor, McKelvie, Samuel James, Ludwig, John Hayes
• Primary Examiner(s): Heinle, Courtney D
• Application Number:
  US15/673,909
• Publication Number:
  US 20180047288A1
• Time in Patent Office: 852 Days
• CPC Class Codes: B60Q 9/008 for anti-collision purposes
• CPC Class Codes: B60R 1/04 mounted inside vehicle B60R1/025, B60R1/08 take precedence
• CPC Class Codes: B60R 1/062 with remote control for adjusting position B60R1/0607 takes precedence
• CPC Class Codes: B60R 2300/105 using multiple cameras
• CPC Class Codes: B60R 2300/8006 for monitoring and displaying scenes of vehicle interior, e.g. for monitoring passengers or cargo
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3605 Destination input or retrieval
• CPC Class Codes: G01S 19/485 whereby the further system is an optical system or imaging system
• CPC Class Codes: G05D 1/0214 in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06V 10/95 structured as a network, e.g. client-server architectures
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/586 of parking space
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G06V 20/59 inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 20/597 : Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 2201/10 : Recognition assisted with metadata
• CPC Class Codes: G06V 40/171 : Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: G06V 40/19 : Sensors therefor
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 : from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/04 : using optical or ultrasonic detectors
• CPC Class Codes: G08G 1/096811 : where the route is computed offboard
• CPC Class Codes: G08G 1/096861 : where the immediate route instructions are output to the driver, e.g. arrow signs for next turn
• CPC Class Codes: G08G 1/096888 : where input information is obtained using learning systems, e.g. history databases
• CPC Class Codes: G08G 1/0969 : having a display in the form of a map
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04N 7/188 : Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position signal generation from motion picture films H04N5/253
• CPC Class Codes: H04W 4/02 : Services making use of location information
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

A method or system is able to refine Global Positioning System (“GPS”) information for guiding a vehicle via extracted metadata using a GPS refinement (“GR”) model managed by a virtuous cycle containing sensors, machine learning center (“MLC”), and a cloud based network (“CBN”). The GR system includes a set of outward facing cameras, a vehicle onboard computer (“VOC”), and GR model. The outward facing cameras mounted on a vehicle are capable of collecting external images representing a surrounding environment in which the vehicle operates. The VOC is configured to generate a positional vehicle location with respect to the surrounding environment in accordance with the external images and historical stored data obtained from CBN. The GR model is configured to generate a driving guidance based on combined information between the positional vehicle location and GPS data.

Claims

1. A system for refining Global Positioning System (“

GPS”

) navigation guidance for a vehicle, comprising;

a plurality of outward facing cameras mounted on the vehicle and configured to capture external images representing a surrounding environment in which the vehicle operates, wherein the surrounding environment includes a road upon which the vehicle is operating;

a vehicle onboard computer (“

VOC”

), coupled to the plurality of outward facing cameras, configured to;

analyze the external images to determine an operating lane of the road in which the vehicle is operating;

analyze the external images to identify at least one reference object;

obtain a size and location of the at least one reference object from a cloud-based network;

generate a positional vehicle location with respect to the surrounding environment and the road based on the operating lane of the vehicle and the size and location of the reference object; and

a GPS refinement (“

GR”

) model coupled to the VOC and configured to;

generate refined GPS navigation information from a combination of GPS location data and the positional vehicle location; and

generate a driving guidance based on the refined GPS navigation information.

2. The system of claim 1, wherein the at least one reference object is at a known location.

3. The system of claim 1, wherein the at least one reference object includes a road sign and the location of the road sign is a surveyed position.

4. The system of claim 1, wherein the driving guidance includes an instruction for a driver of the vehicle to change lanes.

5. The system of claim 1, wherein the positional vehicle location is calculated in response to a computation of two optical rays from two external images captured by two different outward facing cameras.

6. The system of claim 1, wherein the VOC generates the positional vehicle location by being further configured to:

analyze the external images for a plurality of landmarks;

obtain a size and location of each of the plurality of landmarks;

determine a distance from the vehicle to each of the plurality of landmarks based on the size of each of the plurality of landmarks and one or more image capture parameters of the plurality of outward facing cameras; and

determine the positional vehicle location using triangulation between the location of each of the plurality of landmarks and the distance from the vehicle to each of the landmarks.

7. The system of claim 1, wherein the GR model generates the refined GPS navigation information by being further configured to:

determine a next guidance instruction associated with a next exit of the road for the vehicle to follow to a destination;

analyze the external images to identify lane information associated with the next exit of the road;

modify the next guidance instruction based on the lane information and the operating lane of the vehicle; and

output the next guidance instruction to an operator of the vehicle to change lanes or remain in the operating lane for the next exit.

8. The system of claim 1, wherein the GR model identifies the lane information by being further configured to:

analyze the external images to detect at least one lane marking on the road.

9. The system of claim 1, GR model identifies the lane information by being further configured to:

analyze the external images to detect an overhead exit sign with lane markings.