Electronic Toll Management and Vehicle Identification

• US 20130058531A1
• Filed: 09/10/2012
• Published: 03/07/2013
• Est. Priority Date: 02/21/2003
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Hedley, Jay E., Thornburg, Neal Patrick
• Granted Patent: US 8,463,642 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images
• CPC Class Codes: G07B 15/06 Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
• CPC Class Codes: G08G 1/017 identifying vehicles G08G1/015, G08G1/054 take precedence

Abstract

Identifying a vehicle in a toll system includes accessing image data for a first vehicle and obtaining license plate data from the accessed image data for the first vehicle. A set of records is accessed. The license plate data for the first vehicle is compared with the license plate data for vehicles in the set of records. Based on the comparison of the license plate data, a set of vehicles is identified from the vehicles having records in the set of records. Second vehicle identifier data is accessed for the first vehicle and for a vehicle in the set of vehicles. Using a processing device, the second vehicle identifier data for the first vehicle is compared with the second vehicle identifier data for the vehicle in the set of vehicles. The vehicle in the set of vehicles is identified as the first vehicle based on results of the comparison.

Claims

1. A computer-implemented method of identifying a vehicle in a toll system, the method comprising:

accessing image data for a first vehicle;

obtaining license plate data from the accessed image data for the first vehicle;

accessing a set of records that include license plate data for vehicles;

executing, using at least one processing device, a loosened license plate reading algorithm to;

compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records, and

identify a set of vehicle candidates from the vehicles having records in the set of records, the set of vehicle candidates being identified based on results of the comparison of the license plate data, wherein the loosened license plate reading algorithm includes loosened license plate matching criteria or a lowered license plate read confidence threshold to enable generation of a larger set of matching vehicle candidates relative to a license plate reading algorithm designed to identify a single and best vehicle candidate match; and

selecting, from the set of vehicle candidates, a vehicle candidate as corresponding to the first vehicle by;

accessing second vehicle identifier data for the first vehicle, the second vehicle identifier data being data for identifying a vehicle that is distinct from license plate data;

accessing second vehicle identifier data for a vehicle candidate in the set of vehicle candidates,

comparing, using the at least one processing device, the second vehicle identifier data for the first vehicle with the second vehicle identifier data for the vehicle candidate in the set of vehicle candidates, and

identifying the vehicle candidate in the set of vehicle candidates as the first vehicle based on results of the comparison of second vehicle identifier data.

2. The method of claim 1, wherein identifying the vehicle candidate in the set of vehicle candidates as the first vehicle includes identifying the vehicle candidate as the first vehicle if the comparison of the second vehicle identifier data for the first vehicle with the second vehicle identifier data for the vehicle candidate in the set of vehicle candidates indicates a match having a confidence level that exceeds a confidence threshold.

3. The method of claim 2, wherein identifying the vehicle candidate in the set of vehicle candidates as the first vehicle includes identifying the vehicle candidate in the set of vehicle candidates as the first vehicle without human intervention if the confidence level of the match exceeds a first confidence threshold.

4. The method of claim 3, wherein identifying the vehicle candidate in the set of vehicle candidates as the first vehicle includes identifying the vehicle candidate in the set of vehicle candidates as the first vehicle if the confidence level of the match is less than the first confidence threshold but greater than a second confidence threshold and a human operator confirms the match.

5. The method of claim 4, further comprising enabling the human operator to confirm or reject the match by:

enabling the human operator to perceive the accessed image data for the first vehicle, and

enabling the human operator to interact with a user interface to indicate rejection or confirmation of the match.

6. The method of claim 4, wherein identifying the vehicle candidate in the set of vehicle candidates as the first vehicle includes identifying the vehicle candidate as the first vehicle if the confidence level of the match is less than the first and second confidence thresholds and a human operator manually identifies the vehicle candidate as the first vehicle by accessing the image data for the first vehicle and the record for the vehicle in the set of records.

7. The method of claim 1, wherein identifying the vehicle candidate in the set of vehicle candidates as the first vehicle includes identifying the vehicle candidate based on vehicle identification number (VIN), laser signature, inductive signature, and image data.

8. The method of claim 1, wherein identifying a set of vehicle candidates based on the results of the comparison of the license plate data comprises identifying multiple vehicle candidates as corresponding to the first vehicle based on the results of the comparison of the license plate data.

9. The method of claim 1, wherein the license plate reading algorithm comprises an algorithm that reads a license plate number of a target vehicle from an image of the target vehicle and compares the license plate number read from the image to known license plate numbers of vehicles to identify a set of matching vehicle candidates for the target vehicle.

10. The method of claim 1, wherein obtaining license plate data from the accessed image data for the first vehicle comprises obtaining license plate data from the accessed image data using optical character recognition.

12. The method of claim 1, wherein the second vehicle identifier data comprises laser signature data or inductive signature data for the first vehicle.

13. The method of claim 12, wherein the laser signature data includes one or more of an overhead electronic profile of the first vehicle, an axle count of the first vehicle, and a 3D image of the first vehicle.

14. The method of claim 12, wherein the inductive signature data includes one or more of an axle count of the first vehicle, a type of engine of the first vehicle, and a vehicle type or class for the first vehicle.

15. The method of claim 12, wherein the records in the set of records include laser signature data or inductive signature data for vehicles.

11. The method of claim 21, wherein the license plate data includes a license plate number.

16. An apparatus for identifying a vehicle in a toll system, the apparatus comprising:

means for accessing image data for a first vehicle;

means for obtaining license plate data from the accessed image data for the first vehicle;

means for accessing a set of records that include license plate data for vehicles;

means for executing a loosened license plate reading algorithm to;

compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records, and

identify a set of vehicle candidates from the vehicles having records in the set of records, the set of vehicle candidates being identified based on results of the comparison of the license plate data, wherein the loosened license plate reading algorithm includes loosened license plate matching criteria or a lowered license plate read confidence threshold to enable generation of a larger set of matching vehicle candidates relative to a license plate reading algorithm designed to identify a single and best vehicle candidate match; and

means for selecting, from the set of vehicle candidates, a vehicle candidate as corresponding to the first vehicle by;

accessing second vehicle identifier data for the first vehicle, the second vehicle identifier data being data for identifying a vehicle that is distinct from license plate data;

accessing second vehicle identifier data for a vehicle candidate in the set of vehicle candidates,

comparing, using the at least one processing device, the second vehicle identifier data for the first vehicle with the second vehicle identifier data for the vehicle candidate in the set of vehicle candidates, and

identifying the vehicle candidate in the set of vehicle candidates as the first vehicle based on results of the comparison of second vehicle identifier data.

17. An apparatus for identifying a vehicle in a toll system, the apparatus comprising:

an image capture device configured to capture image data for a first vehicle; and

one or more processing devices communicatively coupled to each other and to the image capture device and configured to;

access a set of records that include license plate data for vehicles;

access image data for the first vehicle;

obtain license plate data from the accessed image data for the first vehicle; and

execute a loosened license plate reading algorithm to;

compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records, and

identify a set of vehicle candidates from the vehicles having records in the set of records, the set of vehicle candidates being identified based on results of the comparison of the license plate data, wherein the loosened license plate reading algorithm includes loosened license plate matching criteria or a lowered license plate read confidence threshold to enable generation of a larger set of matching vehicle candidates relative to a license plate reading algorithm designed to identify a single and best vehicle candidate match; and

select, from the set of vehicle candidates, a vehicle candidate as corresponding to the first vehicle by;

accessing second vehicle identifier data for the first vehicle, the second vehicle identifier data being data for identifying a vehicle that is distinct from license plate data;

accessing second vehicle identifier data for a vehicle candidate in the set of vehicle candidates,

comparing, using the at least one processing device, the second vehicle identifier data for the first vehicle with the second vehicle identifier data for the vehicle candidate in the set of vehicle candidates, and

identifying the vehicle candidate in the set of vehicle candidates as the first vehicle based on results of the comparison of second vehicle identifier data.