System and Method for Performing Differential Analysis of Vehicles

• US 20190317517A1
• Filed: 04/13/2018
• Published: 10/17/2019
• Est. Priority Date: 04/13/2018
• Status: Active Grant

• Current Assignee: Blackberry Limited
• Original Assignee: Blackberry Limited
• Inventors: Boulton, Adam John
• Granted Patent: US 10,732,640 B2
• Time in Patent Office: Days
• CPC Class Codes: B60Q 11/005 for lighting devices, e.g. indicating if lamps are burning or not
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2400/00 Indexing codes relating to detected, measured or calculated conditions or factors
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2556/00 Input parameters relating to data
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2754/30 Longitudinal distance
• CPC Class Codes: B60W 2756/10 Involving external transmission of data to or from the vehicle
• 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 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 40/04 Traffic conditions
• 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 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0257 : using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G06F 18/254 : of classification results, e.g. of results related to same input data
• CPC Class Codes: G06T 2207/30236 : Traffic on road, railway or crossing
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/584 : of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/625 : License plates
• CPC Class Codes: G08G 1/0175 : by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

An autonomous or semi-autonomous vehicle may improve the overall safety of other vehicles by determining an action of a proximate vehicle based on sensor data and determining one or more expected visual cues associated with the determined action. One or more images of the proximate vehicle may then be used to detect a difference between expected visual cues and the captured images. Detected differences may be used to notify one or more entities that can then take corrective actions.

Claims

1. A method for vehicle differential analysis at a computing device in a vehicle, the method comprising:

obtaining at least one image associated with a proximate vehicle;

obtaining sensor data associated with the proximate vehicle;

determining a vehicle action of the proximate vehicle from the sensor data;

determining an expected visual cue associated with the determined vehicle action;

detecting a difference between the expected visual cue and the at least one image associated with the proximate vehicle; and

transmitting an indication of the detected difference through a communications interface to a network.

2. The method of claim 1 further comprising obtaining identification information associated with the proximate vehicle.

3. The method of claim 2 wherein the identification information is obtained from the at least one image from a license plate of the proximate vehicle.

4. The method of claim 3 further comprising using optical character recognition on a license plate to determine a vehicle identification information.

5. The method of claim 4 wherein transmitting the indication of the detected difference through the communications interface to a network includes the identification information.

6. The method of claim 1 further comprising performing an action by the vehicle in response to the detected difference.

7. The method of claim 6, wherein the vehicle is an autonomous vehicle, and the action comprises increasing a following distance to the proximate vehicle.

8. The method of claim 6, wherein the vehicle is an autonomous vehicle, and wherein the action comprises changing lanes.

9. The method of claim 1 wherein the sensor data is provided from one or more of a radar unit, an ultrasonic unit, a LIDAR unit and a camera.

10. The method of claim 1 wherein the expected visual cue is a brake light or a turn signal of the proximate vehicle.

11. The method of claim 1 wherein the image is received from a forward facing camera of the vehicle.

12. A computing device in a vehicle for providing differential analysis, the computing device comprising:

a processor; and

a communications subsystem,

wherein the computing device is configured to;

obtain at least one image associated with a proximate vehicle;

obtain sensor data associated with the proximate vehicle;

determine a vehicle action of the proximate vehicle from the sensor data;

determine an expected visual cue associated with the determined vehicle action;

detect a difference between the expected visual cue and the at least one image associated with the proximate vehicle; and

transmit an indication of the detected difference through a communications subsystem to a network.

13. The computing device of claim 12, wherein the computing device is further configured to obtain identification information associated with the proximate vehicle.

14. The computing device of claim 13, wherein the identification information is obtained from the at least one image from a license plate of the proximate vehicle.

15. The computing device of claim 12, wherein the computing device is further configured use optical character recognition on a license plate to determine a vehicle identification information.

16. The computing device of claim 15, wherein transmitting the indication of the detected difference through the communications interface to a network includes the identification information.

17. The computing device of claim 12, wherein the computing device is further configured perform an action by the vehicle in response to the detected difference.

18. The computing device of claim 17, wherein the vehicle is an autonomous vehicle, and the action comprises increasing a following distance to the proximate vehicle.

19. The computing device of claim 17, wherein the vehicle is an autonomous vehicle, and wherein the action comprises changing lanes.

20. The computing device of claim 12, wherein the sensor data is provided from one or more of a radar unit, an ultrasonic unit, a LIDAR unit and a camera.

21. The computing device of claim 12, wherein the expected visual cue is a brake light or a turn signal of the proximate vehicle.

22. The computing device of claim 12, wherein the image is received from a forward facing camera of the vehicle.