CONTROL TRANSFER OF A VEHICLE

• US 20200133290A1
• 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 11,700,356 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 finding at least one trigger for human intervention in a control of a vehicle, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles; determining, based at least on the visual information, the at least one trigger for human intervention; and transmitting to one or more of the plurality of vehicles, the at least one trigger.

Claims

1. A method for finding at least one trigger for human intervention in a control of a vehicle, the method comprises:

receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles;

determining, based at least on the visual information, the at least one trigger for human intervention; and

transmitting to one or more of the plurality of vehicles, the at least one trigger.

2. The method according to claim 1 wherein the determining is executed in an unsupervised manner.

3. The method according to claim 1 wherein the determining is responsive to at least one human intervention policy of the at least one vehicle.

4. The method according to claim 1 wherein the determining of the at least one trigger for human intervention comprises determining a complexity of the situation.

5. The method according to claim 1 wherein the determining of the at least one trigger for human intervention comprises determining a danger level associated with the situation.

6. The method according to claim 1 wherein the determining is responsive to statistics of maneuvers executed by different vehicles during a same situation that is suspected as a situation that requires human intervention in the control of at least one of the plurality of vehicles.

7. The method according to claim 1 comprising generating or receiving movement information of entities included in the visual information;

and wherein the determining of the at least one trigger is also responsive to the movement information.

8. The method according to claim 7 wherein the movement information represents entity movement functions of the entities.

9. The method according to claim 8 comprising estimating, based on the entity movement functions, a future movement of the entities.

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

receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in a control of at least one of the plurality of vehicles;

determining, based at least on the visual information, the at least one trigger for human intervention; and

transmitting to one or more of the plurality of vehicles, the at least one trigger.

11. The non-transitory computer readable medium according to claim 10 wherein the determining is executed in an unsupervised manner.

12. The non-transitory computer readable medium according to claim 10 wherein the determining is responsive to at least one human intervention policy of the at least one vehicle.

13. The non-transitory computer readable medium according to claim 10 wherein the determining of the at least one trigger for human intervention comprises determining a complexity of the situation.

14. The non-transitory computer readable medium according to claim 10 wherein the determining of the at least one trigger for human intervention comprises determining a danger level associated with the situation.

15. The non-transitory computer readable medium according to claim 10 wherein the determining is responsive to statistics of maneuvers executed by different vehicles during a same situation that is suspected as a situation that requires human intervention in the control of at least one of the plurality of vehicles.

16. The non-transitory computer readable medium according to claim 10 that stores instructions for generating or receiving movement information of entities included in the visual information;

and wherein the determining of the at least one trigger is also responsive to the movement information.

17. The non-transitory computer readable medium according to claim 16 wherein the movement information represents entity movement functions of the entities.

18. The non-transitory computer readable medium according to claim 17 comprising estimating, based on the entity movement functions, a future movement of the entities.

19. A computerized system that comprises a processor and multiple units that are configured to receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in a control of at least one of the plurality of vehicles;

determining, based at least on the visual information, the at least one trigger for human intervention; and

transmitting to one or more of the plurality of vehicles, the at least one trigger.