SITUATION BASED PROCESSING

• US 20200130708A1
• Filed: 12/30/2019
• Published: 04/30/2020
• Est. Priority Date: 10/18/2018
• Status: Active Application

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cortica Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Application Number:
  US16/729,589
• Publication Number:
  US 20200130708A1
• Time in Patent Office: Days
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2540/10 Accelerator pedal position
• CPC Class Codes: B60W 2540/12 Brake pedal position
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4023 large-size vehicles, e.g. trucks
• CPC Class Codes: B60W 2554/4046 Behavior, e.g. aggressive or erratic
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/02 related to ambient conditions
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B60W 60/0011 involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/0016 of the vehicle or its occupants
• CPC Class Codes: B60W 60/0017 of other traffic participants
• CPC Class Codes: B60W 60/0025 specially adapted for specific operations
• CPC Class Codes: B60W 60/0051 from occupants to vehicle
• CPC Class Codes: G05B 13/029 using neural networks and expert systems
• CPC Class Codes: G05D 1/0044 : by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps maps used for automatic navigation G05D1/0274; flight directors G01C23/005
• 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 1/0214 : 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 1/0293 : Convoy travelling
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 : Clustering techniques
• CPC Class Codes: G06F 18/23213 : with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/2431 : Multiple classes
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/042 : Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/454 : Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/759 : Region-based matching
• 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 20/584 : of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/597 : Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G07C 5/02 : Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G08G 1/048 : with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
• CPC Class Codes: G08G 1/056 : with provision for distinguishing direction of travel
• CPC Class Codes: G08G 1/09626 : where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• 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: G08G 1/22 : Platooning, i.e. convoy of communicating vehicles
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]

Abstract

A method for situation aware processing, the method may include detecting a situation, based on first sensed information at a first period; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and performing, by a situation related processing unit, a situation related processing, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed at a second period, wherein the situation related processing comprises at least one out of object detection and object behavior estimation,

Claims

1. A method for situation aware processing, the method comprises:

detecting a situation, based on first sensed information at a first period;

selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and

performing, by a situation related processing unit, a situation related processing, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed at a second period, wherein the situation related processing comprises at least one out of object detection and object behavior estimation.

2. The method according to claim 1 wherein the selecting is followed by uploading the situation related subset of the reference information to the situation related processing unit.

3. The method according to claim 1 wherein the performing of the situation related processing comprises ignoring reference information that is not a part of the situation related subset of the reference information.

4. The method according to claim 1 wherein the reference information comprises concept structures, wherein each concept structure comprises multiple signatures and metadata related to the multiple signatures;

wherein the situation related subset of the reference information comprises concept structures related to the situation.

5. The method according to claim 4 comprising:

calculating one or more second signatures of the second sensed information; and

searching, out of the concept structures related to the situation, for at least one matching concept structure that matches the one or more second signatures.

6. The method according to claim 5 comprising determining at least an identity of a detected object based on metadata of the at least one matching concept structure.

7. The method according to claim 5 wherein the detecting of the situation is executed without searching for matching concept structures.

8. The method according to claim 5 wherein the detecting of the situation is executed without calculating signatures of a type used in the object related processing.

9. The method according to claim 1 comprising determining, based on the situation, a relevancy value for each portion out of multiple portions of the second sensed information.

10. The method according to claim 9 comprising performing the situation related processing based on the relevancy value of the each portion.

11. The method according to claim 9 wherein the determining of the relevancy value comprises finding, for a certain type of object, a relevant portion and an irrelevant portion;

and wherein the performing of the situation related processing is executed without searching for the certain type of object within the irrelevant portion.

12. The method according to claim 9 comprising determining a resolution of signatures or concept structures based on the relevancy value of the each portion.

13. The method according to claim 12 wherein the preforming of the situation related processing within the irrelevant portion is executed without using concept structures that describe the object of the certain type.

14. The method according to claim 1 wherein the detecting of the situation is executed by processing the first sensed information in a manner that differs from the performing an object detection, during the situation related processing.

15. The method according to claim 1 wherein the situation related processing is the object detection.

16. The method according to claim 1 wherein the situation related processing is the object behavior estimation.

17. The method according to claim 16 comprising determining, based at least on an outcome of the object behavior estimation and at least one autonomous driving rule, whether to apply autonomous driving or to apply human controlled driving.

18. The method according to claim 1 wherein the detecting of the situation comprises detecting a location of the vehicle.

19. The method according to claim 1 wherein the detecting of the situation comprises detecting at least one out of (a) a location of the vehicle, (b) one or more weather conditions, (c) one or more contextual parameters, (d) a road condition, (e) a traffic parameter.

20. The method according to claim 1 wherein the second period follows the first period.

21. The method according to claim 1 wherein the second period starts before an end of the first period.

22. A system for situation aware processing, the system comprises a situation related processing unit and a memory unit;

wherein the memory unit is configured to store first sensed information sensed at a first period;

wherein the situation related processing unit is configured to;

detect a situation, based on the first sensed information,

select, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and

perform a situation related processing to find detected objects, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed by one or more second vehicle sensors at a second period.

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

detecting a situation, based on first sensed information at a first period;

selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and

performing, by a situation related processing unit, a situation related processing, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed at a second period, wherein the situation related processing comprises at least one out of object detection and object behavior estimation.

24. A method for generating a hybrid representation of a media unit, the method comprises:

receiving or generating the media unit;

processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation;

selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations; and

providing the hybrid representation, wherein the hybrid representation comprises shape information regarding shapes of the media unit regions of interest, and a media unit signature that comprises identifiers that identify the media unit regions of interest.

25. The method according to claim 24 wherein the selecting of the media regions of interest is executed per segment out of multiple segments of the media unit.

26. The method according to claim 24 wherein the shape information comprises polygons that represent shapes that substantially bound the media unit regions of interest.

27. The method according to claim 24 wherein the providing of the hybrid representation of the media unit comprises compressing the shape information of the media unit to provide compressed shape information of the media unit.

28. The method according to claim 27 comprising:

comparing the media unit signature of the media unit to signatures of multiple concept structures to find a matching concept structure that has at least one matching signature that matches to the media unit signature; and

calculating higher accuracy shape information that is related to regions of interest of the media unit, wherein the higher accuracy shape information is of higher accuracy than the compressed shape information of the media unit, wherein the calculating is based on shape information associated with at least some of the matching signatures.

29. The method according to claim 28 comprising determining shapes of the media unit regions of interest using the higher accuracy shape information.

30. The method according to claim 28 wherein for each media unit region of interest, the calculating of the higher accuracy shape information comprises virtually overlaying shapes of corresponding media units of interest of at least some of the matching signatures.