Early warning and collision avoidance

• US 10,854,079 B2
• Filed: 03/14/2019
• Issued: 12/01/2020
• Est. Priority Date: 03/19/2018
• Status: Active Grant

• Current Assignee: Derq Inc.
• Original Assignee: Derq Inc.
• Inventors: Aoude, Georges, Abufadel, Amer, Jeanbart, Karl, Choudhary, Rishabh, Sharma, Ankit
• Primary Examiner(s): Swarthout, Brent
• Application Number:
  US16/353,956
• Publication Number:
  US 20190325754A1
• Time in Patent Office: 628 Days
• Field of Search: 340905, 340907
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G08G 1/005 including pedestrian guidance indicator
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0137 for specific applications
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/017 identifying vehicles G08G1/015, G08G1/054 take precedence
• CPC Class Codes: G08G 1/0175 by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/093 Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/123 indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, tramstransmission of navigation instructions to vehicles G08G1/0968
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/164 Centralised systems, e.g. external to vehicles
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

Claims

1. A method comprising

at equipment that is on board a moving ground transportation entity, receiving safety messages from one or more of (a) roadside equipment located at intersections or along roadways, and (b) other ground transportation entities, including vulnerable road users,

the safety messages from the one or more of the roadside equipment and the other ground transportation entities including information about the location, heading, speed, and predicted future path of one or more of the ground transportation entities,

at the equipment that is on board the ground transportation entity, receiving information about conditions nearby the ground transportation entity from sensors on the ground transportation entity,

(a) acquiring training data from one or more of the roadside equipment and ground transportation entities, the training data representing patterns of motion, behaviors, and intentions of ground transportation entities, and training an artificial intelligence model using the training data to produce a trained artificial intelligence model representing motion, behaviors, and intentions of ground transportation entities, or (b) accessing such an artificial intelligence model that has been trained using training data, or both (a) and (b), and

at the equipment that is on board the ground transportation entity, applying at least one of the received safety messages and received information about conditions nearby the ground transportation entity to the trained artificial intelligence model to generate predictions of at least one of intent and trajectory of one or more other ground transportation entities including vulnerable road users.

2. The method of claim 1 in which the safety messages comprise basic safety messages.

3. The method of claim 1 in which the vulnerable road users comprise users who are not using a motorized vehicle and the safety messages comprise personal safety messages incorporating the location, heading, speed, and future path of the vulnerable road user.

4. The method of claim 1 comprising monitoring one or more occupants of the ground transportation entity and applying results of the monitoring to the artificial intelligence model.

5. The method of claim 4 comprising monitoring behavior of one or more occupants of the ground transportation entity.

6. The method of claim 5 in which monitoring behavior comprises monitoring a conversation.

7. The method of claim 1 comprising monitoring comprises monitoring at least one of a seating sensor, a camera, a hydrocarbon sensor, or a sensor of volatile organic compounds or other toxic materials.

8. The method of claim 1 comprising monitoring a kinematic sensor to measure a reaction or behavior of a driver.

9. The method of claim 1 in which the prediction comprises a prediction that a nearby vehicle will enter a lane change maneuver that creates a risk to the ground transportation entity.

10. The method of claim 1 in which the prediction comprises a prediction of a trajectory of a nearby vehicle.

11. The method of claim 1 comprising, at the equipment that is on board the ground transportation entity, creating a map of static and moving objects in the surroundings of the ground transportation entity.

12. The method of claim 1 in which the monitoring comprises listening to radio transmissions of emergency broadcasts.

13. The method of claim 1 in which the monitoring comprises receiving traffic messages from roadside equipment.

14. The method of claim 1 providing alerts of risks indicated by the predictions of the artificial intelligence model.

15. The method of claim 14 comprising filtering the alerts.

16. The method of claim 15 in which the filtering comprises applying a model to filter based on at least one of a current location of the ground transportation entity, and environmental condition, a driver behavior, a vehicle health or status, or kinematics.

17. The method of claim 16 in which the model was applied to historical data about the responses of a ground transportation entity to received alerts.

18. The method of claim 1 in which the ground transportation entity comprises an autonomous or semi-autonomous ground transportation entity.

19. The method of claim 1 comprising, at the ground transportation entity, sending messages based on the predictions.

20. The method of claim 19 in which the messages are sent to other ground transportation entities.

21. The method of claim 19 in which the messages are sent to roadside equipment.

22. The method of claim 19 in which the messages comprise basic safety messages.

23. The method of claim 1 comprising predicting, at the equipment that is on board the ground transportation entity, intention or trajectory of another ground transportation entity at intersections, approaches to intersections, or crosswalks.

24. The method of claim 1 comprising providing early warnings, at the equipment that is on board the ground transportation entity of dangerous situations to aid collision avoidance.