DETECTING FALLEN CARGO

• US 20200135029A1
• Filed: 12/31/2019
• Published: 04/30/2020
• Est. Priority Date: 10/18/2018
• Status: Active Grant

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Granted Patent: US 11,029,685 B2
• 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 detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

Claims

1. A method for detecting fallen cargo, the method comprises:

receiving by a computerized system, sensed information related to driving sessions of multiple vehicles;

applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes;

estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and

determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

2. The method according to claim 1, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a safety of the vehicle.

3. The method according to claim 1, comprising the applying of the machine learning process on visual information of the sensed information.

4. The method according to claim 1, wherein the estimating of the impact is based at least in part on motion estimation of the vehicle.

5. The method according to claim 1, wherein the estimating of the impact is based at least in part on sensed information sensed by the least one vehicle sensor.

6. The method according to claim 1, wherein the estimating of the impact comprises estimating a level of danger associated with at least some of the fallen cargo classes.

7. The method according to claim 6, wherein the estimating of the level of danger is responsive to a rate of deviation of the vehicles from a previous propagation direction.

8. The method according to claim 6, wherein the estimating of the level of danger is responsive to response of a driver.

9. The method according to claim 1, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a combination of a safety of the vehicle and vehicle fuel consumption.

10. The method according to claim 1, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a combination of a safety of the vehicle and brake deterioration.

11. The method according to claim 1, comprising suggesting a generation of a driver perceivable alert when a fallen cargo is detected.

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

receiving by a computerized system, sensed information related to driving sessions of multiple vehicles;

applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes;

estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and

suggesting, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

13. The non-transitory computer readable medium according to claim 12, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a safety of the vehicle.

14. The non-transitory computer readable medium according to claim 12, that stores instructions for the applying of the machine learning process on visual information of the sensed information.

15. The non-transitory computer readable medium according to claim 12, wherein the estimating of the impact is based at least in part on motion estimation of the vehicle.

16. The non-transitory computer readable medium according to claim 12, wherein the estimating of the impact is based at least in part on sensed information sensed by the least one vehicle sensor.

17. The non-transitory computer readable medium according to claim 12, wherein the estimating of the impact comprises estimating a level of danger associated with at least some of the fallen cargo classes.

18. The non-transitory computer readable medium according to claim 17, wherein the estimating of the level of danger is responsive to a rate of deviation of the vehicles from a previous propagation direction.

19. The non-transitory computer readable medium according to claim 17, wherein the estimating of the level of danger is responsive to response of a driver.

20. The non-transitory computer readable medium according to claim 12, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a combination of a safety of the vehicle and vehicle fuel consumption.

21. The non-transitory computer readable medium according to claim 12, wherein the suggested vehicle behavior is an optimal vehicle behavior in relation to a combination of a safety of the vehicle and brake deterioration.

22. The non-transitory computer readable medium according to claim 12, that stores instructions for suggesting a generation of a driver perceivable alert when a fallen cargo is detected.

23. A computerized system that comprises a processor and multiple units that are configured to

receive sensed information related to driving sessions of multiple vehicles;

apply a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes;

estimate, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and

suggest, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.