LOCATING A VEHICLE BASED ON MULTIMEDIA CONTENT

• US 20200134864A1
• Filed: 08/20/2019
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Granted Patent: US 11,170,233 B2
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0253 extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/246 : using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/762 : using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 : using neural networks
• 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 2201/08 : Detecting or categorising vehicles
• CPC Class Codes: G06V 40/103 : Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G08G 1/162 : event-triggered
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]
• CPC Class Codes: H04W 4/48 : for in-vehicle communication

Abstract

A method for determining a location of a vehicle, the method may include receiving reference visual information that represents multiple reference images acquired at predefined locations; acquiring, by a visual sensor of the vehicle, an acquired image of an environment of the vehicle; generating, based on the acquired image, acquired visual information related to the acquired image, wherein the acquired visual information comprises acquired static visual information that is related to the environment of the vehicle; searching for a selected reference image out of the multiple reference images, the selected reference image comprises selected reference static visual information that best matches the acquired static visual information; and determining an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information; and wherein the determining of the actual location of the vehicle is of a resolution that is smaller than a distance between the selected reference image and a reference image that is immediately followed by the selected reference image.

Claims

1. A method for determining a location of a vehicle, the method comprises:

receiving reference visual information that represents multiple reference images acquired at predefined locations;

acquiring, by a visual sensor of the vehicle, an acquired image of an environment of the vehicle;

generating, based on the acquired image, acquired visual information related to the acquired image, wherein the acquired visual information comprises acquired static visual information that is related to the environment of the vehicle;

searching for a selected reference image out of the multiple reference images, the selected reference image comprises selected reference static visual information that best matches the acquired static visual information; and

determining an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information; and

wherein the determining of the actual location of the vehicle is of a resolution that is smaller than a distance between the selected reference image and a reference image that is immediately followed by the selected reference image.

2. The method according to claim 1 wherein the determining of the actual location of the vehicle comprises calculating a distance between the predefined location of the selected reference image and the actual location of the vehicle based on a relationship between at least one value of a spatial relationship parameter of a first group of pixels within the selected reference image and at least one value of the size parameter of the at least one static object within the acquired image.

3. The method according to claim 2 wherein the determining of the actual location of the vehicle comprises determining whether the actual location of the vehicle precedes the predefined location of the selected reference image or precedes the predefined location of the selected reference image.

4. The method according to claim 3 comprising subtracting the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle precedes the predefined location of the selected reference image.

5. The method according to claim 3 comprising adding the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle follows the predefined location of the selected reference image.

6. The method according to claim 1 wherein the searching for the selected reference image comprises receiving or generating reference images of the same predefined location that differ from each other by scale.

7. The method according to claim 1 wherein each static object of the at least one object is represented by a combination of symbols.

8. The method according to claim 1 wherein a reference visual information of a reference image is a compressed reference visual information that was compressed using a cortex function.

9. The method according to claim 1 wherein the reference visual information comprises signatures of the multiple reference images.

10. The method according to claim 9 wherein a signature of a reference image is a map of firing neurons of a network that fired when the neural network was fed with the reference image.

11. The method according to claim 1 wherein the multiple reference images comprise multiple sets of reference images, wherein different sets of reference images are associated with different predefined locations;

and wherein reference images of a set differ from each other by scale.

12. A non-transitory computer readable medium that stores instructions for:

receiving reference visual information that represents multiple reference images acquired at predefined locations;

acquiring, by a visual sensor of the vehicle, an acquired image of an environment of the vehicle;

generating, based on the acquired image, acquired visual information related to the acquired image, wherein the acquired visual information comprises acquired static visual information that is related to the environment of the vehicle;

searching for a selected reference image out of the multiple reference images, the selected reference image comprises selected reference static visual information that best matches the acquired static visual information; and

determining an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information; and

wherein the determining of the actual location of the vehicle is of a resolution that is smaller than a distance between the selected reference image and a reference image that is immediately followed by the selected reference image.

13. The non-transitory computer readable medium according to claim 12 wherein the determining of the actual location of the vehicle comprises calculating a distance between the predefined location of the selected reference image and the actual location of the vehicle based on a relationship between at least one value of a spatial relationship parameter of a first group of pixels within the selected reference image and at least one value of the size parameter of the at least one static object within the acquired image

14. The non-transitory computer readable medium according to claim 13 wherein the determining of the actual location of the vehicle comprises determining whether the actual location of the vehicle precedes the predefined location of the selected reference image or precedes the predefined location of the selected reference image.

15. The non-transitory computer readable medium according to claim 14 that stores instructions for subtracting the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle precedes the predefined location of the selected reference image.

16. The non-transitory computer readable medium according to claim 14 that stores instructions for adding the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle follows the predefined location of the selected reference image.

17. The non-transitory computer readable medium according to claim 12 wherein the searching for the selected reference image comprises receiving or generating reference images of the same predefined location that differ from each other by scale.

18. The non-transitory computer readable medium according to claim 12 wherein each static object of the at least one object is represented by a combination of symbols.

19. The non-transitory computer readable medium according to claim 12 wherein a reference visual information of a reference image is a compressed reference visual information that was compressed using a cortex function.

20. A system for determining a location of a vehicle, the system comprises:

a communication module configured to receive reference visual information that represents multiple reference images acquired at predefined locations;

a visual sensor of the vehicle that is configured to acquire an acquired image of an environment of the vehicle; and

a processing unit that is configured to generate, based on the acquired image, acquired visual information related to the acquired image, wherein the acquired visual information comprises acquired static visual information that is related to the environment of the vehicle;

search for a selected reference image out of the multiple reference images, the selected reference image comprises selected reference static visual information that best matches the acquired static visual information; and

determine an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information; and

wherein the determining of the actual location of the vehicle is of a resolution that is smaller than a distance between the selected reference image and a reference image that is immediately followed by the selected reference image.