SAFE TRANSFER BETWEEN MANNED AND AUTONOMOUS DRIVING MODES

• US 20200133266A1
• Filed: 12/30/2019
• Published: 04/30/2020
• Est. Priority Date: 10/18/2018
• Status: Active Application

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Application Number:
  US16/729,547
• Publication Number:
  US 20200133266A1
• 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 safe transfer between manned and autonomous driving modes, the method may include detecting, based on first sensed information sensed during a first period, a situation related to an environment of the autonomous vehicle; searching for one or more matching concepts of a group of reference concepts, to which the situation belongs, each reference concept of the group represents a plurality of situations and has a reference concept safety level; wherein for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on a tested success level of at only some of the plurality of scenarios represented by the reference concept; and determining, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment.

Claims

1. A method for safe transfer between manned and autonomous driving modes, the method comprises:

detecting, based on first sensed information sensed during a first period, a situation related to an environment of the autonomous vehicle;

searching for one or more matching concepts of a group of reference concepts, to which the situation belongs,

each reference concept of the group represents a plurality of situations and has a reference concept safety level;

wherein for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on a tested success level of at only some of the plurality of scenarios represented by the reference concept;

determining, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment;

performing a human assisted driving of the vehicle when determining that the autonomous vehicle is not capable to safely autonomously drive through the environment; and

performing an autonomous driving of the vehicle when determining that the autonomous vehicle is capable to safely autonomously drive through the environment.

2. The method according to claim 1, wherein each reference concept comprises reference signatures and reference metadata related to the reference signatures, wherein the method comprises generating a situation signature, and searching for the one or more matching concept s by comparing the situation signature to at least one out of a reference concept signature and reference signatures of the group of reference concepts.

3. The method according to claim 1 wherein at least some of the reference concepts differ from each other by complexity.

4. The method according to claim 1 wherein at least some of the reference concepts differ from each other by resolution.

5. The method according to claim 1 comprising detecting, in proximity to the first period, one or more objects located within the environment of the vehicle.

6. The method according to claim 3 comprising estimating one or more behavior patterns of the one or more objects located within the environment of the vehicle.

7. The method according to claim 4 wherein the determining is also responsive to the one or more behavior patterns.

8. A system for safe transfer between manned and autonomous driving modes, the system comprises a processing unit and a memory unit;

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

wherein the processing unit is configured to;

detect, based on the first sensed information, a situation related to an environment of the autonomous vehicle;

search for one or more matching concepts of a group of reference concepts, to which the situation belongs, wherein each reference concept of the group represents a plurality of situations and has a reference concept safety level, for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on a tested success level of at only some of the plurality of scenarios represented by the reference concept; and

determine, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment.

9. The system according to claim 8, wherein each reference concept comprises reference signatures and reference metadata related to the reference signatures, wherein the method comprises generating a situation signature, and searching for the one or more matching concept s by comparing the situation signature to at least one out of a reference concept signature and reference signatures of the group of reference concepts.

10. The system according to claim 8 wherein at least some of the reference concepts differ from each other by complexity.

11. The system according to claim 8 wherein at least some of the reference concepts differ from each other by resolution.

12. The system according to claim 8 wherein the processing unit is configured to detect, in proximity to the first period, one or more objects located within the environment of the vehicle.

13. The system according to claim 12 wherein the processing unit is configured to estimate one or more behavior patterns of the one or more objects located within the environment of the vehicle.

14. The system according to claim 13 wherein the determining is also responsive to the one or more behavior patterns.

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

detecting, based on first sensed information sensed during a first period, a situation related to an environment of the autonomous vehicle;

searching for one or more matching concepts of a group of reference concepts, to which the situation belongs, each reference concept of the group represents a plurality of situations and has a reference concept safety level, for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on success levels of multiple attempts to autonomously drive one or more autonomous vehicles at only some of the plurality of scenarios represented by the reference concept; and

determine, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment.

16. The non-transitory computer readable medium according to claim 15, wherein each reference concept comprises reference signatures and reference metadata related to the reference signatures, wherein the non-transitory computer readable medium comprises generating a situation signature, and searching for the one or more matching concept s by comparing the situation signature to at least one out of a reference concept signature and reference signatures of the group of reference concepts.

17. The non-transitory computer readable medium according to claim 15 wherein at least some of the reference concepts differ from each other by complexity.

18. The non-transitory computer readable medium according to claim 15 wherein at least some of the reference concepts differ from each other by resolution.

19. The non-transitory computer readable medium according to claim 15 that stores instructions for detecting, in proximity to the first period, one or more objects located within the environment of the vehicle.

20. The non-transitory computer readable medium according to claim 19 that stores instructions for estimating one or more behavior patterns of the one or more objects located within the environment of the vehicle.

21. The non-transitory computer readable medium according to claim 20 wherein the determining is also responsive to the one or more behavior patterns.