SMART VEHICLE

• US 20160357187A1
• Filed: 06/05/2015
• Published: 12/08/2016
• Est. Priority Date: 06/05/2015
• Status: Abandoned Application

• Current Assignee: Bao Tran
• Original Assignee: Arafat M.A. Ansari
• Inventors: ANSARI, ARAFAT M.A.
• Application Number:
  US14/732,459
• Publication Number:
  US 20160357187A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 13/862 Combination of radar systems with sonar systems
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/89 for mapping or imaging
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 15/86 Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
• CPC Class Codes: G01S 15/931 of land vehicles
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9315 Monitoring blind spots
• CPC Class Codes: G01S 2013/9318 Controlling the steering
• CPC Class Codes: G01S 2013/93185 Controlling the brakes
• CPC Class Codes: G01S 2013/9319 Controlling the accelerator
• CPC Class Codes: G01S 2013/9321 Velocity regulation, e.g. cruise control
• CPC Class Codes: G05D 1/0248 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0251 extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06T 19/003 : Navigation within 3D models or images
• CPC Class Codes: G06T 19/006 : Mixed reality object pose determination, tracking or camera calibration for mixed reality G06T7/00
• CPC Class Codes: G06T 19/20 : Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2219/004 : Annotating, labelling
• 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 20/597 : Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G08G 1/0962 : having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/09675 : where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/163 : involving continuous checking
• 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

Abstract

A smart vehicle can be operated by generating a 3D model of a sensor'"'"'s field of view; receiving information from neighboring vehicles to compensate for blindspots in the sensor'"'"'s field of view and in a driver'"'"'s field of view; receiving traffic information, weather information; adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information; aggregating the plurality of 3D models to generate a comprehensive 3D model; and combining the comprehensive 3D model with detailed map information; and using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

Claims

1. A method to operate a vehicle, comprising:

generating a 3D model of a sensor'"'"'s field of view;

receiving information from neighboring vehicles to compensate for blindspots in the sensor'"'"'s field of view and in a driver'"'"'s field of view;

receiving traffic information, weather information;

adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information;

aggregating the plurality of 3D models to generate a comprehensive 3D model; and

combining the comprehensive 3D model with detailed map information; and

using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

2. The method of claim 1, wherein the 3D model of each sensor'"'"'s field of view is based on a pre-determined model of the given sensor'"'"'s unobstructed field of view.

3. The method of claim 1, wherein the 3D model for each sensor'"'"'s field of view is based on the given sensor'"'"'s location and orientation relative to the vehicle.

4. The method of claim 1, wherein the weather information is received from a remote computer or from one of the plurality of sensors.

5. The method of claim 1, wherein:

at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.

6. The method of claim 1, wherein:

the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and this probability data is used when combining the comprehensive 3D model with detailed map information.

7. The method of claim 1, wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle'"'"'s environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.

8. The method of claim 1, wherein information from neighboring vehicles is communicated using a vehicle-to-vehicle transceiver.

9. The method of claim 1, comprising

detecting a vehicle tail-gating the first vehicle by detecting a distance or a time period separating the first and second vehicle;

and

modifying a buffer zone separating the first and second vehicles to avoid tail-gating.

10. The method of claim 1, comprising detecting a new road from a positioning system, capturing street view images of the new road, and updating a digital map with the new road coordinates and street view images.

11. A method for navigating a first vehicle, comprising:

generating a 3D model of a sensor'"'"'s field of view;

detecting a vehicle tail-gating the first vehicle by detecting a distance or a time period separating the first and second vehicle;

modifying a buffer zone separating the first and second vehicles to avoid tail-gating.

12. The method of claim 11, wherein the 3D model of each sensor'"'"'s field of view is based on a pre-determined model of the given sensor'"'"'s unobstructed field of view.

13. The method of claim 11, wherein the 3D model for each sensor'"'"'s field of view is based on the given sensor'"'"'s location and orientation relative to the vehicle.

14. The method of claim 11, wherein the weather information is received from a remote computer or from one of the plurality of sensors.

15. The method of claim 11, wherein:

at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.

16. The method of claim 11, wherein:

the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and this probability data is used when combining the comprehensive 3D model with detailed map information.

17. The method of claim 11, wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle'"'"'s environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.

18. The method of claim 11, wherein information from neighboring vehicles is communicated using a vehicle-to-vehicle transceiver.

19. The method of claim 11, comprising:

receiving information from neighboring vehicles, traffic information, and weather information;

adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and tailgating information;

aggregating the plurality of 3D models to generate a comprehensive 3D model;

combining the comprehensive 3D model with detailed map information; and

using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

20. The method of claim 11, comprising detecting a new road from a positioning system, capturing street view images of the new road, and updating a digital map with the new road coordinates and street view images.