VEHICLE TO VEHICLE (V2V) COMMUNICATION LESS TRUCK PLATOONING

• US 20200133308A1
• Filed: 12/28/2019
• Published: 04/30/2020
• Est. Priority Date: 10/18/2018
• Status: Abandoned Application

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Application Number:
  US16/729,320
• Publication Number:
  US 20200133308A1
• 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

V2V communication-less truck platooning.

Claims

1. A method for vehicle to vehicle communication less truck platooning, the method comprises:

monitoring, by a truck monitor, an environment of the truck to provide monitoring results;

determining, based on the sensed information, whether to engage in truck platooning with a set of at least one other truck;

engaging the truck platooning following determination to engage in the truck platooning;

wherein the engaging comprises at least partially autonomously driving the truck based on the monitoring results.

2. The method according to claim 1 comprising determining, based at least on the monitoring results, whether disengage the truck platooning.

3. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on a risk level associated with the truck platooning.

4. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on a comparison between the path of progress of the set and a desired path of progress of the truck.

5. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on an assessment of a risk level associated with the progress of the set.

6. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on an assessment of a risk level associated with road conditions.

7. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on an assessment of a risk level imposed by an entrance of a vehicle between members of a convoy.

8. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on a physiological state of a driver of the truck.

9. The method according to claim 2 wherein at least one of the determining of whether to engage and whether to disengage is based on at least one out of (a) a relationship between a type of the vehicle and a type of the certain truck, (b) a relationship between a size of the vehicle and a size of the certain truck, and (c) a relationship between a weight of the vehicle and a weight of the certain truck.

10. The method according to claim 2 wherein the engaging the truck platooning comprises engaging as a leading truck.

11. The method according to claim 2 wherein the engaging the truck platooning comprises engaging as a following truck.

12. The method according to claim 2 wherein the engaging comprises engaging the platooning at certain location, and then replacing locations with at least one other truck.

13. The method according to claim 1 comprising receiving commands conveyed over vehicle to vehicle communication, wherein the commands are for determining driving of trucks while engaged in the truck platooning.

14. The method according to claim 13, wherein the at least partially autonomously driving the truck is executed regardless of the commands.

15. The method according to claim 13, comprising analyzing an accuracy of the commands and generating an alerts when the commands are inaccurate.

16. The method according to claim 13, comprising analyzing an accuracy of the commands and disengaging when the commands are inaccurate.

17. The method according to claim 1 comprising dynamically determining am amount of control of the vehicle during the at least partially autonomously driving of the truck.

18. A non-transitory computer readable medium for vehicle to vehicle communication less truck platooning, the non-transitory computer readable medium stores instructions for:

monitoring, by a truck monitor, an environment of the truck to provide monitoring results;

determining, based on the sensed information, whether to engage in truck platooning with a set of at least one other truck;

engaging the truck platooning following determination to engage in the truck platooning;

wherein the engaging comprises at least partially autonomously driving the truck based on the monitoring results.

19. A system for vehicle to vehicle communication less truck platooning, the system comprises:

a truck monitor that is configured to;

monitor an environment of the truck to provide monitoring results;

determine, based on the sensed information, whether to engage in truck platooning with a set of at least one other truck;

and participate in engaging the truck platooning following determination to engage in the truck platooning;

wherein the engaging comprises at least partially autonomously driving the truck based on the monitoring results.