Source: http://www.google.com/patents/US7970644?ie=ISO-8859-1&dq=7,496,943
Timestamp: 2014-08-01 12:28:23
Document Index: 115635382

Matched Legal Cases: ['Application No. 60', 'Application No. 200680027002', 'Application No. 0608926', 'application No. 2004213923', 'Application No. 200480010404', 'Application No. 2']

Patent US7970644 - Electronic toll management and vehicle identification - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsIdentifying 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. Each record includes license plate data for a vehicle. The license plate data for the first vehicle...http://www.google.com/patents/US7970644?utm_source=gb-gplus-sharePatent US7970644 - Electronic toll management and vehicle identificationAdvanced Patent SearchPublication numberUS7970644 B2Publication typeGrantApplication numberUS 11/423,683Publication dateJun 28, 2011Filing dateJun 12, 2006Priority dateFeb 21, 2003Also published asUS20060278705Publication number11423683, 423683, US 7970644 B2, US 7970644B2, US-B2-7970644, US7970644 B2, US7970644B2InventorsJay E. Hedley, Neal Patrick ThornburgOriginal AssigneeAccenture Global Services LimitedExport CitationBiBTeX, EndNote, RefManPatent Citations (61), Non-Patent Citations (19), Referenced by (21), Classifications (5), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetElectronic toll management and vehicle identificationUS 7970644 B2Abstract 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. Each record includes license plate data for a vehicle. 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 results of the comparison of the license plate data, a set of vehicles is identified from the vehicles having records in the set of records. Vehicle fingerprint data is accessed for the first vehicle. The vehicle fingerprint data for the first vehicle is based on the image data for the first vehicle. Vehicle fingerprint data for a vehicle in the set of vehicles is accessed. Using a processing device, the vehicle fingerprint data for the first vehicle is compared with the vehicle fingerprint 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 of vehicle fingerprint data.
1. A 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, each record including license plate data for a vehicle;
comparing the license plate data for the first vehicle with the license plate data for vehicles in the set of records;
identifying a set of vehicles from the vehicles having records in the set of records, the set of vehicles being identified based on results of the comparison of the license plate data;
accessing vehicle fingerprint data for the first vehicle, the vehicle fingerprint data for the first vehicle being based on the accessed image data for the first vehicle;
accessing vehicle fingerprint data for a vehicle in the set of vehicles;
comparing, using a processing device, the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles; and
identifying the vehicle in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data,
wherein comparing the license plate data for the first vehicle with the license plate data for vehicles in the set of records includes searching a vehicle record database for records that include license plate data that exactly match the license plate data obtained for the first vehicle, and
wherein comparing the license plate data for the first vehicle with the license plate data for vehicles in the set of records includes performing an extended search of the vehicle record database for records that include license plate data that nearly match the license plate data obtained for the first vehicle, the extended search being conditioned on no vehicle identification records being found that include license plate data that exactly match the license plate data obtained for the first vehicle.
2. The method of claim 1, wherein comparing the license plate data for the first vehicle with the license plate data for vehicles in the set of records includes comparing the license plate data using predetermined matching criteria.
3. The method of claim 2, further comprising changing the predetermined matching criteria to increase the number of vehicles in the identified set of vehicles.
4. The method of claim 3, wherein changing the predetermined matching criteria to increase the number of vehicles in the identified set of vehicles is conditioned on a failure to identify any vehicles in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data.
5. The method of claim 1, further comprising accessing laser signature data or inductive signature data for the first vehicle.
6. The method of claim 5, wherein the laser signature data comprises data obtained by using a laser to scan the first vehicle.
7. The method of claim 5, 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.
8. The method of claim 5, wherein the inductive signature data comprises data obtained through use of a loop array over which the first vehicle passes.
9. The method of claim 5, 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.
10. The method of claim 5, wherein each record in the set of records includes laser signature data or inductive signature data for a vehicle.
11. The method of claim 10, further comprising comparing laser signature data or inductive signature data for the first vehicle with laser signature data or inductive signature data for vehicles in the set of records.
12. The method of claim 11, wherein identifying a set of vehicles from the vehicles having records in the set of records includes identifying the set of vehicles based on the results of the comparison of the license plate data and the results of the comparison of the laser signature data or the inductive signature data.
13. The method of claim 12, wherein identifying the set of vehicles based on the results of the comparison of the license plate data and the results of the comparison of the laser signature data or inductive signature data includes determining a combined equivalent matching score for each vehicle having a record in the set of records and identifying the set of vehicles as a set of vehicles having combined equivalent matching scores above a predetermined threshold.
14. The method of claim 13, wherein each combined equivalent matching score comprises a weighted combination of a laser or inductive signature matching score and a license plate matching score.
15. The method of claim 11, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle as the first vehicle based on the results of the comparison of the vehicle fingerprint data and the results of the comparison of the laser signature data or inductive signature data.
16. The method of claim 15, wherein identifying the vehicle in the set of vehicles as the first vehicle based on the results of the comparison of the vehicle fingerprint data and the results of the comparison of the laser signature data or inductive signature data includes determining a combined equivalent matching score for the vehicle in the set of vehicles and determining that the combined equivalent matching score is higher than a predetermined threshold.
17. The method of claim 16, wherein the combined equivalent matching score comprises a weighted combination of a laser or inductive signature matching score and a vehicle fingerprint matching score.
18. The method of claim 1, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle as the first vehicle if the comparison of the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles indicates a match having a confidence level that exceeds a confidence threshold.
19. The method of claim 18, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle in the set of vehicles as the first vehicle without human intervention if the confidence level of the match exceeds a first confidence threshold.
20. The method of claim 19, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle in the set of vehicles 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.
21. The method of claim 20, 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.
22. The method of claim 20, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle 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 as the first vehicle by accessing the image data for the first vehicle and the record for the vehicle in the set of records.
23. The method of claim 22, further comprising enabling the human operator to manually identify the vehicle in the set of vehicles as the first vehicle by:
enabling the human operator to access the image data for the first vehicle,
enabling the human operator to access the record for the vehicle in the set of records, and
enabling the human operator to interact with a user interface to indicate positive identification of the first vehicle as the vehicle in the set of vehicles.
24. The method of claim 23, further comprising enabling the human operator to manually identify the vehicle in the set of vehicles as the first vehicle by enabling the human operator to access data stored in databases of external systems.
25. The method of claim 1, wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle by combining vehicle identification number (VIN), laser signature, inductive signature, and image data.
26. An article comprising a machine-readable storage device storing machine-executable instructions that, when applied to a machine, cause the machine to perform operations comprising:
comparing the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles; and
27. 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:
obtain license plate data from the captured image data for the first vehicle;
access a set of records, each record including license plate data for a vehicle;
compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records;
identify a set of vehicles from the vehicles having records in the set of records, the set of vehicles being identified based on results of the comparison of the license plate data;
access vehicle fingerprint data for the first vehicle, the vehicle fingerprint data for the first vehicle being based on the captured image data for the first vehicle;
access vehicle fingerprint data for a vehicle in the set of vehicles;
compare the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles; and
identify the vehicle in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data,
wherein the one or more processing devices being configured to compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records includes the one or more processing devices being configured to search a vehicle record database for records that include license plate data that exactly match the license plate data obtained for the first vehicle, and
wherein the one or more processing devices being configured to compare the license plate data for the first vehicle with the license plate data for vehicles in the set of records includes the one or more processing devices being configured to perform an extended search of the vehicle record database for records that include license plate data that nearly match the license plate data obtained for the first vehicle, the extended search being conditioned on no vehicle identification records being found that include license plate data that exactly match the license plate data obtained for the first vehicle.
28. A method of identifying a vehicle in a toll system, the method comprising:
accessing laser signature data or inductive signature data for the first vehicle;
accessing a set of records, each record including license plate data for a vehicle and laser signature data or inductive signature data for a vehicle;
comparing laser signature data or inductive signature data for the first vehicle with laser signature data or inductive signature data for vehicles in the set of records,
identifying a set of vehicles from the vehicles having records in the set of records, the set of vehicles being identified based on results of the comparison of the license plate data and the results of the comparison of the laser signature data or the inductive signature data;
wherein identifying the set of vehicles based on the results of the comparison of the license plate data and the results of the comparison of the laser signature data or inductive signature data includes determining a combined equivalent matching score for each vehicle having a record in the set of records and identifying the set of vehicles as a set of vehicles having combined equivalent matching scores above a predetermined threshold.
29. The method of claim 28, wherein each combined equivalent matching score comprises a weighted combination of a laser or inductive signature matching score and a license plate matching score.
30. A method of identifying a vehicle in a toll system, the method comprising:
identifying the vehicle in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data and results of the comparison of the laser signature or inductive signature data,
wherein identifying the vehicle in the set of vehicles as the first vehicle based on the results of the comparison of the vehicle fingerprint data and the results of the comparison of the laser signature data or inductive signature data includes determining a combined equivalent matching score for the vehicle in the set of vehicles and determining that the combined equivalent matching score is higher than a predetermined threshold.
31. The method of claim 30, wherein the combined equivalent matching score comprises a weighted combination of a laser or inductive signature matching score and a vehicle fingerprint matching score.
32. A method of identifying a vehicle in a toll system, the method comprising:
wherein identifying the vehicle in the set of vehicles as the first vehicle includes identifying the vehicle by combining vehicle identification number (VIN), laser signature, inductive signature, and image data. Description
CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application Ser. No. 10/371,549, filed on Feb. 21, 2003, and titled ELECTRONIC TOLL MANAGEMENT, hereby incorporated by reference in its entirety for all purposes. This application claims priority to U.S. Provisional Patent Application No. 60/689,050, filed on Jun. 10, 2005, and titled ELECTRONIC TOLL MANAGEMENT, hereby incorporated by reference in its entirety for all purposes.
TECHNICAL FIELD This document relates to electronic toll management.
BACKGROUND Transportation facilities such as roads, bridges, and tunnels produce tolls often representing a major source of income for many states and municipalities. The large number of automobiles, trucks, and buses stopping at tollbooths to pay a toll daily can cause significant problems. For example, such facilities may restrict the flow of traffic causing traffic backups and lane changing, often increasing the likelihood of accidents and even more bottlenecks. In addition, many people may be delayed from reaching their destinations, and goods may be delayed from getting to market and millions of gallons of fuel may be wasted as vehicles idle. Environments may experience an increase in pollution as idling and slow moving vehicles emit pollutants (particularly carbon dioxide and carbon monoxide), which may pose a significant health hazard to motorists as well as to tollbooth operators.
SUMMARY The present disclosure describes at least one toll system that enables automatic and electronic handling of payment of tolls by vehicles passing a toll facility, without requiring the vehicles to slow down or to have a transponder. Such a system automatically identifies all or substantially all of the vehicles that pass the toll facility, and bills the owner of each identified vehicle for the incurred toll fee. Unfortunately, due to the high number of vehicles that pass through a typical toll facility, known vehicle identification techniques (e.g., license plate reading (LPR)) typically have too high of an error rate for effective use in this system. For example, the error rate for a typical LPR system may be approximately 1%. While such an error rate may be acceptable for toll systems that only identify vehicles that are violators, this error rate is typically too high for a toll system that attempts to identify every passing vehicle, not just the violators, for collection of toll fees. In such a system, a 1% error rate can result in a significant loss of revenue (e.g., the loss of 1000 or more toll fees a day).
Additionally, typical LPR systems often exhibit a tradeoff between the number of vehicles identified (i.e., those vehicles for which the read result exceeds a read confidence threshold for presumption of correct ID) and the error rate. In an ideal world, this tradeoff would be reflected in a binary confidence continuum, where the system always produces a read confidence level of one when the read result is correct and a read confidence level of zero when the read result is incorrect. In reality, however, the read results are usually at least partially correct, and the system generates a confidence continuum having a broad range of confidence levels ranging, for example, from a level of one or near one (very likely correct) to a level of zero or near zero (very likely incorrect). The system, therefore, is often required to set an arbitrary read confidence threshold for determining which read results will be deemed correct. Once the read confidence threshold is set, any read results having confidence levels above the threshold are deemed correct and any read results having confidence levels below the threshold are deemed incorrect. Setting the read confidence threshold too high (e.g., at 0.95 or higher) significantly decreases the possibility of an error but also excludes many correct read results, thereby reducing revenue. Conversely, setting the read confidence threshold too low (e.g., 0.3 or higher) increases the number of reads deemed correct but also significantly increases the number of errors, thereby increasing costs by introducing errors into a large number of accounts/bills which require much time and effort to audit and correct. In a toll system that identifies every passing vehicle, this tradeoff is particularly problematic since it may result in a significant loss of profits.
Moreover, a toll system that identifies every passing vehicle is identifying a much larger number of vehicles than a conventional toll system, which typically only identifies violators. Accordingly, such a toll system attempts to identify every passing vehicle and is designed to both maximize revenue by identifying vehicles very accurately and limit personnel costs by minimizing the need for manual identification of vehicles and account/bill error processing.
In one particular implementation, to obtain a lower vehicle identification error rate (and obtain a higher automated identification rate), the toll system uses two vehicle identifiers to identify a target vehicle. Specifically, the toll system collects image and/or sensor data for the target vehicle and extracts two vehicle identifiers from the collected data. The vehicle identifiers extracted from the collected data may include, for example, license plate information, a vehicle fingerprint, a laser signature, and an inductive signature for the target vehicle. In one particular implementation, the first vehicle identifier is license plate information and the second vehicle identifier is a vehicle fingerprint.
The toll system uses the first vehicle identifier to determine a set of one or more matching vehicle candidates by searching a vehicle record database and including in the set only those vehicles associated with records having data that match or nearly match the first vehicle identifier of the target vehicle. The toll system uses the second vehicle identifier of the target vehicle to identify the target vehicle from among the set of matching vehicle candidates.
When the first vehicle identifier is license plate information and the second vehicle identifier is a vehicle fingerprint, the toll system may eliminate the problematic trade-off between the number of vehicles identified and the error rate typical of LPR systems by using the LPR identification for identification of the group of vehicle candidates, rather than for the final identification of the vehicle, and then using the much more accurate vehicle fingerprint matching for the final identification of the vehicle. Thus, incorrect reads by the LPR system are eliminated during the final and more accurate fingerprint matching identification. This toll system may thereby be able to obtain extremely accurate identification results for a larger proportion of vehicles than would be obtained through license plate reading alone.
In particular, the toll system accesses the records of the matching vehicle candidates and searches for one or more records that have data sufficiently similar to the second vehicle identifier of the target vehicle so as to indicate a possible match. If no possible matches are found for the target vehicle among the set of matching vehicle candidates, the toll system may increase the size of the set by changing the matching criteria and may once again attempt to identify one or more possible matches for the target vehicle from among the larger set of matching vehicle candidates. If still no possible matches are found, the toll system may enable a user to manually identify the target vehicle by providing the user with access to the collected data for the target vehicle and access to databases internal and/or external to the toll system.
If one or more possible matches are found, a confidence level is determined for each possible match. If the confidence level of a possible match surpasses an automated confidence threshold, the toll system automatically identifies the target vehicle without human intervention as the vehicle corresponding to the possible match. If the confidence level of a possible match surpasses a probable match threshold, the toll system presents the probable match to a human operator and enables the human operator to confirm or reject the probable match. If no automatic match or confirmed probable match is found, the toll system enables a user to manually identify the target vehicle by providing the user with access to the collected data for the target vehicle and the possible matches identified by the toll system, and with access to databases internal and/or external to the toll system.
In this manner, the toll system typically obtains greater vehicle identification accuracy by requiring that two vehicle identifiers be successfully matched for successful vehicle identification. Moreover, the identification process may be faster because the matching of the second identifier is limited to only those vehicle candidates having records that successfully match the first vehicle identifier. Human operator intervention is also kept to a minimum through use of multiple confidence level thresholds.
In one general aspect, 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. Each record includes license plate data for a vehicle. 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 results of the comparison of the license plate data, a set of vehicles is identified from the vehicles having records in the set of records. Vehicle fingerprint data is accessed for the first vehicle. The vehicle fingerprint data for the first vehicle is based on the image data for the first vehicle. Vehicle fingerprint data for a vehicle in the set of vehicles is accessed. Using a processing device, the vehicle fingerprint data for the first vehicle is compared with the vehicle fingerprint 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 of vehicle fingerprint data.
Implementations may include one or more of the following features. For example, comparing license plate data for the first vehicle with license plate data for vehicles in the set of records may include searching a vehicle record database for records that include license plate data that exactly match the license plate data obtained for the first vehicle. Comparing license plate data for the first vehicle may further include performing an extended search of the vehicle record database for records that include license plate data that nearly match the license plate data obtained for the first vehicle. The extended search may be conditioned on no vehicle identification records being found that include license plate data that exactly match the license plate data obtained for the first vehicle.
Comparing the license plate data for the first vehicle with the license plate data for vehicles in the set of records may include comparing the license plate data using predetermined matching criteria. The predetermined matching criteria may be changed to increase the number of vehicles in the identified set of vehicles. Changing the predetermined matching criteria to increase the number of vehicles in the identified set of vehicles may be conditioned on a failure to identify any vehicles in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data.
Identifying a vehicle in a toll system may further include capturing laser signature data or inductive signature data for the first vehicle. The laser signature data may include data obtained by using a laser to scan the first vehicle. The laser signature data may include 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.
The inductive signature data may include data obtained through use of a loop array over which the first vehicle passes. The inductive signature data may include 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.
Each record in the set of records includes laser signature data or inductive signature data for a vehicle. Identifying a vehicle in a toll system may further include comparing laser signature data or inductive signature data for the first vehicle with laser signature data or inductive signature data for vehicles in the set of records. Identifying a set of vehicles from the vehicles having records in the set of records may include identifying the set of vehicles based on the results of the comparison of the license plate data and the results of the comparison of the laser signature data or the inductive signature data.
Identifying the set of vehicles based on the results of the comparison of license plate data and the results of the comparison of the laser signature data or inductive signature data may include determining a combined equivalent matching score for each vehicle having a record in the set of records and identifying the set of vehicles as a set of vehicles having combined equivalent matching scores above a predetermined threshold. Each combined equivalent matching score may include a weighted combination of a laser or inductive signature matching score and a license plate matching score.
Identifying the vehicle in the set of vehicles as the first vehicle may include identifying the vehicle as the first vehicle based on the results of the comparison of the vehicle fingerprint data and the results of the comparison of the laser signature data or inductive signature data. Identifying the vehicle in the set of vehicles as the first vehicle based on the results of the comparison of the vehicle fingerprint data and the results of the comparison of the laser signature data or inductive signature data may include determining a combined equivalent matching score for the vehicle in the set of vehicles and determining that the combined equivalent matching score is above a predetermined threshold. The combined equivalent matching score may include a weighted combination of a laser or inductive signature matching score and a vehicle fingerprint matching score.
Identifying the vehicle in the set of vehicles as the first vehicle may include identifying the vehicle as the first vehicle if the comparison of the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles indicates a match having a confidence level that exceeds a confidence threshold. Identifying the vehicle in the set of vehicles as the first vehicle may include identifying the vehicle as the first vehicle without human intervention if the confidence level of the match exceeds a first confidence threshold and/or may include identifying the vehicle as the first vehicle if the confidence level of the match is less than the first confidence level but greater than a second confidence threshold and a human operator confirms the match. The human operator may confirm or reject the match by enabling the operator to perceive the 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.
Identifying the vehicle in the set of vehicles as the first vehicle may include identifying the vehicle 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 as the first vehicle by accessing the image data for the first vehicle and the record for the vehicle in the set of records. The human operator may manually identify the vehicle in the set of vehicles as the first vehicle by enabling the human operator to access the image data for the first vehicle, enabling the human operator to access the record for the vehicle in the set of records, and enabling the human operator to interact with a user interface to indicate positive identification of the first vehicle as the vehicle in the set of vehicles. The human operator may be enabled to manually identify the vehicle in the set of vehicles as the first vehicle by enabling the human operator to access data stored in databases of external systems.
Identifying the vehicle in the set of vehicles as the first vehicle may include identifying the vehicle by combining vehicle identification number (VIN), laser signature, inductive signature, and image data.
In another general aspect, an apparatus for identifying a vehicle in a toll system includes an image capture device configured to capture image data for a first vehicle. The apparatus further includes one or more processing devices communicatively coupled to each other and to the image capture device. The one or more processing devices are configured to obtain license plate data from the captured image data for the first vehicle and access a set of records. Each record in the set of records includes license plate data for a vehicle. The one or more processing devices are further configured 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 vehicles from the vehicles having records in the set of records. The set of vehicles is identified based on results of the comparison of the license plate data. The one or more processing devices are further configured to access vehicle fingerprint data for the first vehicle. The vehicle fingerprint data for the first vehicle is based on the captured image data for the first vehicle. The one or more processing devices are also configured to access vehicle fingerprint data for a vehicle in the set of vehicles, compare the vehicle fingerprint data for the first vehicle with the vehicle fingerprint data for the vehicle in the set of vehicles, and identify the vehicle in the set of vehicles as the first vehicle based on results of the comparison of vehicle fingerprint data.
In another general aspect, identifying a vehicle in a toll system includes accessing image or sensor data for a target vehicle and extracting a first identifier and a second identifier from the image or sensor data. The extracted first identifier is used to identify a set of one or more vehicle candidates as potential matches for the target vehicle. The extracted second identifier is used to identify the target vehicle as a vehicle selected from the set of one or more vehicle candidates.
The above and other implementations and features are described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of an implementation of an electronic toll management system.
DETAILED DESCRIPTION FIG. 1 is a block diagram of an implementation of an electronic toll management system 10. The system 10 is configured to capture a vehicle identifier 31 of vehicle 30 interacting with a facility 28 and to notify external systems 34 of such interaction. For example, the system 10 may allow a toll road authority to capture a vehicle identifier 31, such as license plate information, from a vehicle 30 traveling through the toll road and then to notify law enforcement whether the captured vehicle identifier matches a license plate previously highlighted by law enforcement.
(1) Laser/sonic/microwave devices�these devices, commonly used in Intelligent Transportation Systems (ITS) applications, can recognize the presence of a vehicle and provide information regarding the vehicle's size, classification, and/or speed. These sensors may be configured to provide additional information about the vehicle which can be used in identify the vehicle and its use of the toll facility, including trip time and compliance with traffic laws.
(2) Loops�these sensors can detect the presence and the vehicle type by recognizing the presence of metal masses using a wire loop embedded in the road. Loops can be used as a backup to more sophisticated sensors. Loops can also be used as a primary source of data to detect vehicles, classify vehicles, trigger cameras, and provide vehicle signature data (e.g., based on use of an array of loops with a smart loop control program such as Diamond Consulting's IDRIS� system of Buckinghamshire, United Kingdom).
(3) Through-beam sensors�these sensors may emit a continuous beam across the roadway, and detect the presence of a vehicle based upon interruptions in the beam. This type of sensor may be used in installations where traffic is channeled into tollbooth-style lanes.
(4) Optical sensors�vehicle may be recognized using cameras to continuously monitor images of the roadway for changes indicating the presence of a vehicle. These cameras also can be used to record images for vehicle identification.
The customer management module 26 can allow a user to interact with the toll management computer 12 over a communications channel such as a computer network (e.g., Internet, wired, wireless, etc.), a telephone connection, or other channel. The user can include a party associated with a vehicle 22 (e.g., owner of the vehicle), a public or private authority responsible for management of the facility 28, or other user. The customer management module 26 includes a combination of hardware and software module configured to handle customer interactions such as an account management module 26 a, a dispute management module 26 b and a payment processing module 26 c. The module 26 employs secure access techniques such as encryption, firewalls, password or other techniques.
The account management module 26 a allows users such as motorists to create an account with the system 10, associate multiple vehicles with that account, view transactions for the account, view images associated with those transactions, and make payments on the account. In one implementation, a user responsible for the facility can access billing and collection information associated with motorists that have used the facility.
The dispute management module 26 b may permit customers to dispute specific transactions on their accounts and to resolve disputes using the computer 12 or third parties. Disputes may arise during billing situations. The module 26 b may help resolve such disputes in an automated fashion. The module 26 b can provide a customer to access an �eResolution� section of a controlling/billing authority website. Customers can file a dispute and download an image of their transaction, the one in question. If there is no match (i.e., the customers automobile is not the automobile in the photo frame), the bill can be forwarded for a third party evaluation such as arbitration. In the far more likely case, the photo will show that the customer's automobile was indeed billed correctly. Dispute management can use encrypted security in which all text and images are sent over a computer network (e.g., the Internet) using high strength encryption. Proof of presence images can be embedded into the dispute resolution communication as an electronic watermark.
The payment processing module 26 c provides functionality for processing payments manually or electronically, depending on the remittance received. For example, if payment remittance is in the form of a paper check, then scanning devices could be used to convert the paper information into electronic format for further processing. On the other hand if electronic payment is employed, then standard electronic payment techniques can be used. The payment processing module 26 c can support billing methods such as traditional mailing, electronic payment (e.g. using a credit card, debit card, smart card, or Automated Clearing House transaction), periodic billing (e.g., send the bill monthly, quarterly, upon reaching a threshold, or other). The payment processing module 26 c can support discounts and surcharges based on frequency of usage, method of payment, or time of facility usage. The payment processing module 26 c also can support payment collection methods such as traditional check processing, processing payment during renewal of a vehicle registration (with interest accrued), electronic payment, direct debit bank, credit cards, pre-payment, customer-initiated payments (as often as the customer desires), or provide discounts for different purposes.
The highlighted vehicle identifier database 20 can be used to store the lists provided by the law enforcement agencies. The term �highlighted� refers to the notion that the law enforcement agencies have provided a list of vehicle identifiers that the agencies have indicated (highlighted) they wish the toll facility to monitor. For example, when a motor vehicle is stolen and reported to police, the police can send a list of highlighted vehicle identifiers to the database 20. When the vehicle highlighted by the police travels through facility, the imaging processing module 24 determines a vehicle identifier associated with the vehicle and determines through certain interfaces that the particular vehicle is being sought by law enforcement. The law enforcement authorities may wish to be instantly notified of the location of the vehicle (and driver), the time it was detected at the location, and the direction it was headed. The computer 12 can notify in substantially real-time mobile units associated with law enforcement. In addition, law enforcement can automatically highlight vehicles based upon the expiration of a license, occurrence of a traffic court date, or other event. This could, in turn, help keep illegal drivers off the road and increase revenue to the state.
If the computer handles payment processing, the computer 12 processes (block 216) a payment response from the party associated with the vehicle identifier. In one implementation, the billing database 16, in conjunction with the billing engine 22 and the customer management module 26, can be used to handle billing and collection functions. As discussed above, the payment processing module 26 c can support electronic or manual payment processing depending on the remittance received. For example, the computer 12 can provide an account for handling electronic payment processing over a computer network such as the Internet. The computer can also handle traditional payment receipt such as a check.
The computer 12 provides (block 302) an account for a party associated with the vehicle identifier. In one embodiment, the computer 12 in conjunction with the account management module 26 a can provide a website for customers to open an account for making electronic payment over a computer network such as the Internet. The website also can permit the customer to access and update account information such as payment history, payment amount due, preferred payment method, or other information.
The computer 12 receives (block 304) a request over a communications channel from the party to review a transaction event. For example, the account payment module 26 a can handle this request by retrieving transaction event information associated with the customer's account from the billing database 16. The retrieved information can include image data of a particular transaction involving the customer's vehicle and the tollbooth.
On the other hand, if the computer 12 determines (block 310) that the party does not agrees to pay, then the computer 12 processes (block 312) a payment dispute request from the party. In one implementation, the dispute management module 26 b can handle a dispute request submitted by the customer using online techniques. The module 26 b can handle specific transactions related to the customer's account including involving a third party to resolve the dispute.
Another example of a vehicle identifier is a vehicle detection tag as described in U.S. Pat. No. 6,747,687, hereby incorporated by reference in its entirety for all purposes. The vehicle detection tag, hereinafter referred to as a vehicle fingerprint, is a distilled set of data artifacts that represent the visual signature of the vehicle. The image processing module 625 may generate a vehicle fingerprint by processing an image of the vehicle. To save on processing time and storage needs however, the generated vehicle fingerprint typically does not include the normal �picture� information that a human would recognize. Accordingly, it is usually not possible process the vehicle fingerprint to obtain the original vehicle image. Some vehicle fingerprints, however, may include normal picture information. A vehicle fingerprint typically may be used to uniquely identify the vehicle.
In one implementation, a camera in the image acquisition module 624 captures a single �still� image of the back of each vehicle that passes the toll facility 628. For each vehicle, the image processing module 625 recognizes the visual cues that are unique to the vehicle and reduces them into a vehicle fingerprint. Because a license plate is a very unique feature, the image processing module 625 typically maximizes the use of the license plate in creating the vehicle fingerprint. Notably, the vehicle fingerprint also includes other parts of the vehicle in addition to the license plate and, therefore, vehicle identification through matching of vehicle fingerprints is generally considered more accurate than vehicle identification through license plate information matching. The vehicle fingerprint may include, for example, portions of the vehicle around the license plate and/or parts of the bumper and the wheelbase.
FIG. 7 is a flow chart of an exemplary two-tier identification process 700 that may be implemented to increase the accuracy of vehicle identification. Image and/or sensor data is captured for a vehicle that interacts with a toll facility (hereinafter referred to as the �target vehicle�) and two vehicle identifiers are extracted from the captured data (block 710). In one implementation, only image data is collected and the two vehicle identifiers extracted are a license plate number and a vehicle fingerprint. In another implementation, image data and inductive sensor data are collected and the vehicle identifiers extracted are the vehicle fingerprint and the inductive signature.
In some implementations, the toll management system may be purposefully designed to identify a larger set of matching vehicle candidates during operation 720 to, for example, ensure that the expected lesser accuracy of vehicle identification through the first identifier does not erroneously result in exclusion of the target vehicle from the set of matching vehicle candidates. For example, if the first vehicle identifier is a license plate number, the license plate reading algorithm may be intentionally modified in, for example, two ways: (1) the matching criteria of the license plate reading algorithm may be loosened to enable the algorithm to generate a larger set of matching vehicle candidates and (2) the license plate reading algorithm may be �detuned� by lowering the read confidence threshold used to determine whether a read result is included in the matching candidate set. For instance, the license plate reading algorithm may be loosened to only require a matching vehicle candidate to match a subset or lesser number of the characters in the license plate number extracted for the target vehicle. Additionally or alternatively, the read confidence threshold may be lowered to enable previously suspected incorrect reads (i.e., partial or low confidence reads) to be included in the matching vehicle candidate set.
In one implementation, the computer 612 identifies a vehicle in part through use of an electronic signature that includes a laser signature and/or an inductive (i.e., magnetic) signature. When a vehicle transacts with the toll system, an electronic signature is captured for the vehicle. The image and measurements of the vehicle created by the laser (i.e., the laser signature) and/or the magnetic scan (i.e., the inductive signature) are compared against known dimensions and images of vehicles based on vehicle identification number (VIN) that were, for example, previously captured by the toll system or by an external system. By comparing the electronic signature image and dimensions to known dimensions of vehicles based on VIN, the search for a matching vehicle and associated VIN may be narrowed. If, for example, an LPR for the vehicle has a low confidence level, but the electronic signature of the vehicle has been captured, the toll system may access a database, as described above, of known dimensions and images for vehicles and associated VINs and cross reference the electronic signature dimensions and images against the database to identify the matching vehicle VIN or identify potential matching vehicle candidates/VINs. The read errors database 6183 links previous incorrect read results to correct vehicle identification records. For example, when automated vehicle identification fails but manual vehicle identification succeeds, the captured vehicle identification data (e.g., the license plate read result) that led to an �error� (i.e., an identification failure) by the automated system is stored in an error record in the read errors database 6183 that is linked to the vehicle identification record that was manually identified for the vehicle. Thus, when the same vehicle identification data is captured again at a later date, the computer 612 may successfully identify the vehicle automatically by accessing the error record in the read errors database 6183, which identifies the correct vehicle identification record for the vehicle, without requiring another manual identification of the vehicle.
An error record also may be generated and stored in the read errors database 6183 when automated identification of the vehicle succeeds based on a near match of an incorrect license plate read result. For example, if the license plate number �ABC123� is read as �ABC128� and the identified candidate match set is �ABC128,� �ABC123,� �ABG128� and �ABC128� which in turn yields the correct match of �ABC123,� an error record may be created that automatically links a license plate read result of �ABC128� to the vehicle having the license plate number �ABC123.�
The computer 612 performs an extended search by changing or loosening the criteria for a successful match or detuning the license plate read algorithm. For example, the computer 612 may perform an extended search by one or more of the following: (1) comparing a subset of the license plate number read result with the characters of the license plate numbers stored in the vehicle record database 6182 (e.g., the last two characters of the license plate number may be omitted such that if the license plate number is �ABC123,� any vehicles having license plate numbers �ABC1**� are deemed matching candidates, wherein �*� is a variable); (2) comparing a subset of the license plate number read result in reverse order with the characters of the license plate numbers stored in the vehicle record database 6182 in reverse order (e.g., the last two characters of the license plate number in reverse order may be omitted such that if the license plate number is �ABC123�, which is �321CBA� in reverse order, any vehicles having license plate numbers in reverse order of �321C**� are deemed matching candidates, wherein �*� is a variable); and (3) other near match techniques including comparing modified versions of the license plate read result and license plate numbers stored in the vehicle record database 6182 in which some of either or both are substituted and/or removed to reduce the impact of misread characters. For example, if the OCR algorithm does not indicate a confidence level above a predetermined threshold in a read result of a character on the license plate, that character may be ignored. Additionally or alternatively, if the OCR algorithm indicates that a character on the license plate may be one of two possible different characters, both alternative characters may be used in the extended search.
The systems and techniques described here can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or an Web browser through which a user can interact with an implementation of the invention, or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (�LAN�), a wide area network (�WAN�), and the Internet.
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