CN112750220A - Method, device and system for detecting intentional fee evasion of vehicle and storage medium - Google Patents
Method, device and system for detecting intentional fee evasion of vehicle and storage medium Download PDFInfo
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- CN112750220A CN112750220A CN202110053514.3A CN202110053514A CN112750220A CN 112750220 A CN112750220 A CN 112750220A CN 202110053514 A CN202110053514 A CN 202110053514A CN 112750220 A CN112750220 A CN 112750220A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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Abstract
The invention provides a method, a device, a system and a storage medium for detecting vehicle intentional fee evasion, comprising the following steps: acquiring vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle in a driving direction, and the vehicle information comprises at least one of image information, position information and time information; extracting target vehicle characteristics based on the acquired vehicle information of the target vehicle and the first vehicle; inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has intentional fee evasion behaviors or not by the detection machine learning model. The method can effectively distinguish the vehicle with the purpose fee evasion from the vehicle with the purpose fee evasion, and take corresponding measures to the vehicle with the purpose fee evasion.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a method, a device and a system for detecting intentional fee evasion of a vehicle and a storage medium.
Background
With the continuous increase of motor vehicle reserves in China, the traditional manual Toll Collection mode cannot meet the requirement of the existing rapid passing, more and more vehicle owners select an Electronic Toll Collection (ETC) lane at a high speed to rapidly pay for passing, and in urban traffic, an unattended parking Toll Collection mode is also provided by an intelligent parking lot in combination with various payment platforms, so that the passing efficiency is improved, and the labor cost is saved.
The system comprises a high-speed ETC system, an intelligent parking lot, a toll collection lane, a toll collection vehicle and a toll collection vehicle, wherein the high-speed ETC system and the intelligent parking lot belong to the unattended toll collection lane, vehicles which pass through without paying tolls often appear in the operation process, and all the vehicles belong to toll collection vehicles, but some vehicles in the toll collection vehicles are toll collection vehicles caused by system abnormality or other non-subjective factors; other vehicles use the existing loopholes in the toll collection system to intentionally escape.
According to the record of unpaid fee, the vehicle can only judge whether the vehicle is the same type of fee evasion vehicle, but can not distinguish the vehicle.
Disclosure of Invention
The embodiment of the invention provides a method, a device and a system for detecting vehicle intentional fee evasion and a storage medium, which can solve the problem that the vehicle unintentional fee evasion and the vehicle intentional fee evasion cannot be distinguished.
In a first aspect, an embodiment of the present invention provides a method for detecting a vehicle intentional fee evasion, including:
acquiring vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle in a driving direction; extracting target vehicle characteristics based on the acquired vehicle information of the target vehicle and the first vehicle; inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has intentional fee evasion behaviors or not by the detection machine learning model.
Optionally, the target vehicle characteristic includes a license plate status characteristic and/or a vehicle body status characteristic. Correspondingly, the extracting the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes: and extracting the license plate state characteristics and/or the vehicle body state characteristics of the target vehicle based on the acquired image information of the target vehicle.
Optionally, the target vehicle characteristic includes a target vehicle characteristic and a driving behavior characteristic. Correspondingly, the extracting the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes: calculating a following distance characteristic of a target vehicle and a first vehicle based on the acquired position information of the target vehicle and the position information of the first vehicle, wherein the target vehicle and the first vehicle are adjacent vehicles. And extracting the driving behavior characteristics of the target vehicle based on the acquired position information of the target vehicle in the process of passing through the lane intercepting device.
Optionally, the target vehicle characteristic comprises a driving speed characteristic. Correspondingly, the extracting the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes: and calculating the running speed of the target vehicle based on the acquired time of the target vehicle reaching a plurality of preset positions.
Optionally, before the inputting the target vehicle feature into a pre-trained intentional fee evasion detection machine learning model, the detecting machine learning model determining whether there is an intentional fee evasion behavior of the target vehicle, further includes: the method comprises the steps of obtaining vehicle information of an actual passing vehicle, matching the vehicle information of the actual passing vehicle with vehicle information paid for, and taking the actual passing vehicle as a target vehicle when matching fails.
In a second aspect, an embodiment of the present invention provides a vehicle intentional fee evasion detection device, including: an acquisition unit configured to acquire vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a preceding vehicle of the target vehicle in a traveling direction; an extraction unit configured to extract a target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle; and the judging unit is used for inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors or not.
In a third aspect, an embodiment of the present invention provides a vehicle intentional fee evasion detection system, including: the system comprises a vehicle position detection device, a license plate recognition device, an interception device and a data processing module, wherein the vehicle position detection device, the license plate recognition device and the interception device are respectively connected with the data processing module, the vehicle position detection device is used for detecting the positions of a first vehicle and a target vehicle and the driving behavior of the target vehicle in the process of passing through the interception device, and transmitting the positions of the first vehicle and the target vehicle and the driving behavior to the data processing module; the license plate recognition device is used for detecting the license plate state of the target vehicle and transmitting the license plate state to the data processing module; and the data processing module is used for inputting the positions, the driving behaviors and the license plate states of the first vehicle and the target vehicle into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors or not.
Optionally, the method further includes: the vehicle position detection device is also used for acquiring vehicle information of an actual passing vehicle and transmitting the vehicle information of the actual passing vehicle to the data processing module; the charging module is used for transmitting the paid vehicle information to the data processing module; and the data processing module is used for receiving the actual passing vehicle and the paid vehicle information and carrying out matching analysis on the vehicle information of the actual passing vehicle and the paid vehicle information.
Optionally, the system further comprises a support, wherein a vehicle position detection device and a communication antenna module for communication connection with a target vehicle are arranged on the support, and the communication antenna module is connected with the charging module.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium, which, when run on a terminal device, causes the terminal device to execute the vehicle intentional fee evasion detection method of any one of the first aspects.
It is understood that the beneficial effects of the second to fourth aspects can be seen from the description of the first aspect, and are not described herein again.
According to the embodiment of the invention, the following distance between the target vehicle and the first vehicle is calculated according to the acquired position of the target vehicle and the position of the first vehicle; calculating the running speed of the target vehicle based on the acquired time for the target vehicle to reach a plurality of preset positions; acquiring the license plate state of a target vehicle, wherein the specific license plate state can be a normal license plate, an unlinked license plate or a shielded license plate; acquiring driving behaviors of a target vehicle in the process of passing through a lane intercepting device, wherein the specific driving behaviors comprise normal behaviors, parking behaviors or backing behaviors; therefore, the acquired information is input into a pre-trained intentional fee evasion detection machine learning model, whether the target vehicle has an intentional fee evasion behavior is judged through the detection machine learning model, whether the target vehicle is an intentional fee evasion vehicle is further judged, and corresponding measures are taken for the intentional fee evasion vehicle.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of a vehicle intentional fee evasion detection system according to an embodiment of the present invention;
FIG. 2 is a second schematic structural diagram of a system for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a device for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a device for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present invention and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The inventor finds that the most common intentional fee evasion means at present have the following steps in the process of researching intentional fee evasion and unintentional fee evasion, firstly, a target vehicle passes through a vehicle which is adjacent to the front and normally passes by utilizing a certain width of a drop bar coil in a toll lane, and a system judges that the two vehicles are the same vehicle by mistake; and secondly, triggering the anti-smashing function of the intercepting device to forcibly lift the intercepting device so as to achieve the purpose of fee evasion and passing.
Aiming at the two fee evasion means, processing measures are correspondingly adopted at present, wherein firstly, a grating is adopted to replace a coil, and secondly, a high-speed interception device is used to increase the difficulty of fee evasion; however, the above-mentioned measures cannot completely prevent the behavior of intentional fee evasion; and other vehicle fee evasion detection equipment is used for comparing the charging record running water to position the vehicle which is not paid, and then the vehicle is realized by late-stage additional payment, but the mode cannot distinguish between unintentional fee evasion and intentional fee evasion.
Because the vehicle which is unintentionally escaped from the fee belongs to the unintentional behavior, the vehicle owner is only required to be informed to pay the fee; however, the vehicle which intentionally escapes is a malicious act and may be given a certain penalty if necessary. For a vehicle with an intentional fee evasion, there are usually a large number of records of fee evasion, and the driving law of the vehicle can be analyzed through the records to determine the vehicle with the intentional fee evasion.
The vehicle intentional fee evasion detection system provided by the embodiment of the invention comprises: the vehicle position detection device, the license plate recognition device, the interception device and the data processing module are respectively connected with the data processing module, wherein the vehicle position detection device can be used for detecting the positions of the first vehicle and the target vehicle and the driving behavior of the target vehicle in the process of passing through the interception device, and transmitting the positions of the first vehicle and the target vehicle and the driving behavior to the data processing module; the specific vehicle position detection means may be provided as a laser sensor, an ultrasonic sensor, a video detector, or the like, which detects the position. The license plate recognition device can extract and recognize the license plate state of a target vehicle in motion from a complex background, can be used for detecting the license plate state of the target vehicle and transmits the license plate state to the data processing module; and the data processing module can be used for inputting the positions, the driving behaviors and the license plate states of the first vehicle and the target vehicle into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has an intentional fee evasion behavior or not through the detection machine learning model.
In the embodiment of the invention, the vehicle position detection device detects the positions and the driving behaviors of the target vehicle and the first vehicle, the license plate recognition device detects the license plate state, and the information is input into the pre-trained intentional fee evasion detection machine learning model through the data processing module, so that the fee evasion vehicle can be judged to be an unintentional fee evasion or an intentional fee evasion.
In the context of the present invention, the front is the opposite direction to the direction of travel of the target vehicle, and the rear is the direction of travel of the target vehicle.
In some possible embodiments, referring to fig. 1, fig. 1 shows a schematic structural diagram of a vehicle intentional fee evasion detection system in an ETC lane, which includes a support 8, an interception device 7, a vehicle position detection device 1, a license plate recognition device 2, a toll collection module 4 such as an ETC toll collection system, a communication antenna module 3 such as an ETC antenna and a data processing module 5, wherein the vehicle position detection device 1, the license plate recognition device 2, the toll collection module 4 and the interception device 7 are respectively connected with the data processing module 5.
In the present embodiment, the vehicle position detection device 1 can specifically detect the position by a laser sensor, an ultrasonic sensor, a video detector, or the like. For example, when a laser sensor is arranged to detect a position, a plurality of laser transmitters and a plurality of laser receivers may be arranged, and when a target vehicle is shielded from laser irradiation, the time when the target vehicle passes through the laser transmitters may be collected. In addition, in the embodiment, the number of passing vehicles can be counted by arranging the laser sensor, the toll collection record number transmitted by the toll collection module 4 is compared with the actually detected passing data, the passing vehicles without the toll collection can be obtained, and then the passing data without the toll collection is input into the trained intentional fee evasion detection machine learning model, so that whether the target vehicle intentionally evades the toll can be judged.
The license plate recognition device 2 can be a license plate recognizer and is used for recognizing license plate numbers of vehicles and detecting actual suspension states of the license plates. Certainly, the license plate recognition device 2 can also be set as a shooting device for shooting the license plate state and detecting the actual hanging state of the license plate; the system can be matched with a road side unit working at low power for obtaining the unique MAC code or the electronic license plate of a vehicle-mounted unit of the vehicle, can also be other devices capable of obtaining the identification information of the vehicle, such as RFID, and can also be used for identifying the electronic license plate of the vehicle, and the detection of the electronic license plate can be used as a follow-up payment basis.
The intercepting device 7 may be a railing machine, a rod machine, a barrier gate, an electric retractable door, or other devices that can be used to intercept the passing of vehicles, which is not limited in this embodiment.
Specifically, in the present embodiment, the vehicle position detection device 1 and the communication antenna module 3 are disposed at the top of the bracket 8, so that the detection range can be increased; the scanning direction of the vehicle position detection apparatus 1 may be scanning in the traveling direction of the target vehicle 9 for detecting the positions of the target vehicle 9 and the first vehicle 10. The intercepting apparatus 7 may be disposed at a rear position of the support frame 8, and a certain interval, such as 3m, may be disposed between the intercepting apparatus 7 and the support frame 8. The support 8 and the toll collection module 4 can be arranged on one side of the lane, and the license plate recognition device 2 can be arranged on the other side of the lane. To safeguard the charging module 4 and the data processing module 5, both may be provided in the sentry box 6.
In other possible embodiments, please refer to fig. 2, fig. 2 shows a schematic structural diagram of a system for detecting intentional fee evasion of a vehicle in an intelligent parking lot, which includes a vehicle position detecting device 1, a license plate recognizing device 2, an intercepting device 3, a charging module 4 and a data processing module 5, wherein the vehicle position detecting device 1, the license plate recognizing device 2, the charging module 4 and the intercepting device 3 are respectively connected with the data processing module 5 in a communication manner, which may be a wired communication connection, such as HDMI, USB, RS485 or RS232, or a wireless communication connection, such as bluetooth or WiFi; for example, the vehicle position detection device 1, the license plate recognition device 2, the data processing module 5 and the toll collection module 4 can be connected to the same switch through internet access.
Specifically, the vehicle position detection device 1 and the license plate recognition device 2 may be installed on the same side of the lane; the intercepting device 3 can be installed at a position about 1 meter behind the vehicle position detecting device 1, and the license plate recognition device 2 can be installed at a position about 3 meters behind the intercepting device 3. To safeguard the charging module 4 and the data processing module 5, both may be provided in the sentry box 6.
Fig. 3 shows a schematic flowchart of a method for detecting vehicle intentional fee evasion according to an embodiment of the present invention, specifically:
s100, vehicle information of a target vehicle and a first vehicle is obtained, wherein the first vehicle is a vehicle ahead of the target vehicle along the driving direction, and the vehicle information comprises at least one of image information, position information and time information.
It is understood that the positions of the target vehicle and the first vehicle may be detected by a vehicle position detecting device, and the vehicle position detecting device transmits the detected position information to the data processing module for analysis processing. Specifically, the position can be detected by a laser sensor, an ultrasonic sensor, a video detector, or the like. And can record the time when the vehicle position detection means detects the position. For example, when a laser sensor is provided to detect a position, a plurality of laser transmitters and a plurality of laser receivers may be provided, and when the target vehicle or the first vehicle passes by, laser irradiation is blocked, and the time when the target vehicle passes by this laser transmitter may be collected. The embodiment can acquire the image information of the vehicle through the image acquisition device, such as a photographing device, a camera device and the like.
And S200, extracting the characteristics of the target vehicle based on the acquired vehicle information of the target vehicle and the first vehicle.
In some embodiments, the target vehicle feature may be extracted based on the acquired location information.
The target vehicle characteristics include a following distance characteristic and a driving behavior characteristic, and correspondingly, the step S200 may include:
s210, calculating the following distance characteristics of the target vehicle and the first vehicle based on the acquired position information of the target vehicle and the position information of the first vehicle, wherein the target vehicle and the first vehicle are adjacent vehicles.
As an example, the position of the target vehicle may be a head position of the target vehicle, the position of the first vehicle may be a tail position of the first vehicle, and a distance between the head position of the target vehicle and the tail position of the first vehicle is used as a following distance which is input into the machine learning model as a following distance feature; in this case, the distance between the target vehicle and the first vehicle is relatively smaller, and is more consistent with the scanning range of the laser sensor.
In a possible implementation manner, step S210 specifically includes:
s211, acquiring a first position of the target vehicle and a third position of the first vehicle corresponding to the first position, and calculating a first sub-vehicle following distance between the target vehicle and the first vehicle;
s212, the second position of the target vehicle and the fourth position of the first vehicle corresponding to the second position are obtained, and the second sub-following distance between the target vehicle and the first vehicle is calculated.
When the target vehicle is located at different positions, calculating a vehicle distance between the target vehicle and the first vehicle, namely a first sub vehicle following distance and a second sub vehicle following distance, and taking the first sub vehicle following distance and the second sub vehicle following distance as input data of a pre-trained intentional fee evasion detection machine learning model.
Of course, more than two positions of the target vehicle can be obtained, more than two positions corresponding to the first vehicle can be obtained, and more than two following distances can be obtained through calculation.
By way of example and not limitation, the position of the intercepting device is taken as an origin, the front Y1 and the front Y2 of the intercepting device are set as line measuring points, Y1 and Y2 are line measuring points at different positions, for example, Y1 is 5m, Y2 is 2m, and the scanning range of the laser sensor is-10 m. For example, Y1 is 4m, Y2 is 1m, and the scanning range of the laser sensor is-5 m to 5 m. When the laser sensor detects that the head of the target vehicle reaches the position Y1, the position Y3 of the tail of the first vehicle, namely the adjacent preceding vehicle, is detected at the same time, when the laser sensor detects that the head of the target vehicle reaches the position Y2, the position Y4 of the tail of the first vehicle, namely the adjacent preceding vehicle, is detected at the same time, and when the head of the target vehicle is calculated to be Y1 and Y2 respectively, the following distances to the preceding vehicle are respectively D1-Y1-Y3 and D2-Y2-Y4. It can be understood that, when the vehicle head of the target vehicle reaches the position Y1 or Y2, the distance between the target vehicle and the first vehicle may be relatively long, and when the laser sensor detects that the vehicle head of the target vehicle reaches the position Y1 or Y2, for example, the first vehicle is not in the scanning range set by the laser sensor, such as-10 m to 10m, the position of the vehicle tail of the first vehicle may be set to-10 m; for another example, if the first vehicle is not in the scanning range set by the laser sensor, such as-5 m to 5m, the position of the tail of the first vehicle can be set to-5 m, and the distance between the head of the target vehicle and the tail of the first vehicle can be calculated.
And S220, extracting the driving behavior characteristics of the target vehicle based on the acquired position information of the target vehicle in the process of passing through the lane blocking device. The driving behavior includes normal, parking, or reverse behavior, and O may be used to represent the driving behavior. And inputting the driving behavior as the driving behavior characteristic into the machine learning model.
When the head of the target vehicle reaches the position of the intercepting device and the tail of the target vehicle completely passes through the position of the intercepting device, the time period is taken as the time period of the target vehicle passing through the lane intercepting device; the vehicle position detection means detects the traveling behavior of the target vehicle during this time period.
In other embodiments, the target vehicle feature may be extracted based on the acquired time information.
The target vehicle characteristic includes a running speed characteristic, and accordingly, the step S200 may include:
and S230, calculating the running speed of the target vehicle based on the acquired time of the target vehicle reaching a plurality of preset positions.
The time of the target vehicle reaching the preset position can be detected by adopting a vehicle position detection device, and the time is sent to the data processing module; the travel speed is then entered into the machine learning model as a travel speed characteristic.
Specifically, the running speed may include a first sub-running speed and a second sub-running speed, and accordingly, the step S230 includes,
s231, calculating a first sub-running speed of the target vehicle based on the acquired time for the target vehicle to sequentially reach a first preset position and a second preset position;
s232, calculating a second sub-running speed of the target vehicle based on the acquired time for the target vehicle to sequentially reach a third preset position and a fourth preset position; the first preset position, the second preset position, the third preset position and the fourth preset position are a plurality of preset positions arranged along the driving direction.
And when the target vehicle is positioned at different position sections, calculating the running speed of the target vehicle, namely a first sub-running speed and a second sub-running speed, and using the first sub-running speed and the second sub-running speed as input data of the pre-trained intentional fee evasion detection machine learning model.
By way of example and not limitation, the position of the intercepting device is taken as an origin, and X1, X2, X3 and X4 in front of the intercepting device are four points to be measured at different positions, for example, X1 is 6m, X2 is 5m, X3 is 2m, X4 is 1m, and further, for example, X1 is 5m, X2 is 4m, X3 is 1.5m, and X4 is 0.5 m. When the vehicle position detection device detects that the vehicle head of the target vehicle reaches the above four positions, the times at that time are recorded as T1, T2, T3 and T4, respectively, the traveling speed of the target vehicle between Y1 and Y2 is calculated as V1 ═ X1-X2)/(T2-T1, and the traveling speed between Y3 and Y4 is calculated as V2 ═ X3-X4)/(T4-T3.
In still other embodiments, the target vehicle feature may be extracted based on the acquired image information.
As an example, the target vehicle characteristics may include a license plate status characteristic and/or a vehicle body status characteristic, and a driving speed characteristic, and accordingly, the step S200 may include:
s240, extracting license plate state characteristics and/or vehicle body state characteristics of the target vehicle based on the acquired image information of the target vehicle;
by way of illustration, the license plate status features include a normal license plate, an unlinked license plate, and an occluded license plate, and the license plate status can be represented using S. For example, a place M in front of the license plate recognition device, such as 5M or 3M, is selected as a snapshot point, when the vehicle position detection device detects that the head of the target vehicle reaches the snapshot point, the position information of the target vehicle is sent to the data processing module, and the data processing module triggers the license plate recognition device to detect the license plate state of the target vehicle.
The vehicle body state characteristics comprise vehicle body forms of cars, buses and trucks, the fee evasion probabilities of different types of vehicles are different, and the fee evasion probabilities of trucks are higher due to higher toll and higher fee evasion probabilities, so that the accuracy of judgment can be improved by acquiring the vehicle body states and taking the vehicle body states as input characteristics.
S300, inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has intentional fee evasion behaviors or not by the detection machine learning model. The vehicle with the intentional fee evasion can be effectively distinguished.
In some feasible embodiments, the trigger state information of the anti-smashing function of the barrier gate can be acquired and input into the training model, so that the accuracy of the judgment result is improved.
In other possible embodiments, the state information of the On Board Unit (OBU) of the target vehicle may be detected, and the OBU state information may include a detached state, an unplugged state, a no-entry state, and the like, and is input into the training model, so as to improve the accuracy of the determination result.
It should be noted that the pre-trained intentional fee evasion detection machine learning model is trained by the following steps:
s410, extracting the vehicle characteristics of the fee evasion data sample, and constructing a characteristic vector;
it is understood that the feature vector may be constructed from the first and second sub following distances D1 and D2, the first and second sub traveling speeds V1 and V2, the license plate state S, and the traveling behavior O of the target vehicle;
and S420, training the fee evasion data sample to obtain a pre-trained intentional fee evasion detection machine learning model.
The fee evasion data samples can be trained by adopting a classification algorithm based on the combination of a neural network and a decision tree, and certainly, other algorithms can also be adopted for training, such as a regression algorithm, a Bayesian method and the like. The method comprises the following steps that main general manual audits for unintentional fee evasion and intentional fee evasion are distinguished from fee evasion sample data, for example, a single vehicle often appears in fee evasion data, or a vehicle with obvious intentional fee evasion behaviors through manual observation of vehicle videos can be used as an intentional fee evasion sample; the fee evasion sample which appears when the ETC system has faults can be used as an unintentional fee evasion sample; samples which cannot be manually judged whether to intentionally escape can be temporarily removed and are not put into training samples, so that the precision of the final training model is improved.
According to the embodiment of the invention, by acquiring the target vehicle characteristics of the target vehicle and inputting the acquired information into the pre-trained intentional fee evasion detection machine learning model, whether the target vehicle has intentional fee evasion behavior can be judged through the detection machine learning model, so that whether the target vehicle is an intentional fee evasion vehicle is judged, and corresponding measures are taken for the intentional fee evasion vehicle, so that the economic loss is reduced.
In a possible implementation manner, before step S300, the method further includes:
and S500, acquiring the vehicle information of the actual passing vehicle, matching the vehicle information of the actual passing vehicle with the paid vehicle information, and taking the actual passing vehicle as a target vehicle when the matching fails.
When the matching fails, the actual passing vehicle is not paid, and is an unintentional fee evasion vehicle or an intentional fee evasion vehicle, the vehicle is taken as a target vehicle, the vehicle information of the target vehicle is input into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has an intentional fee evasion behavior, so that the vehicle can be judged to be unintentional fee evasion or intentional fee evasion. And when the matching is successful, the actual passing vehicle is paid, and the vehicle is a normal passing vehicle. The vehicle information comprises a following distance, a driving speed, a license plate state and a driving behavior.
As an example, the laser sensor may be used to count the actual passing vehicles, and the payment record number recorded by the ETC charging system or the parking lot charging system is compared with the actually detected passing data, so as to obtain the passing vehicle without payment, and then the vehicle information of the passing target vehicle without payment is input into the pre-trained intentional fee evasion detection machine learning model, so as to determine whether the target vehicle intentionally evades the fee.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 4 shows a block diagram of a vehicle intentional fee evasion detection device provided in the embodiment of the present invention, which corresponds to the vehicle intentional fee evasion detection method in the above embodiment, and only the relevant parts to the embodiment of the present invention are shown for convenience of description.
Referring to fig. 4, the apparatus includes:
an acquisition unit 41 configured to acquire vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a preceding vehicle of the target vehicle in a traveling direction;
an extraction unit 42 configured to extract a target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle;
the first judging unit 43 is configured to input the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors.
In some possible embodiments, the extraction unit 42 of the device further comprises:
and the following distance calculation unit is used for calculating the following distance characteristics of the target vehicle and the first vehicle based on the acquired position information of the target vehicle and the position information of the first vehicle, wherein the target vehicle and the first vehicle are adjacent vehicles.
As an example, the following distance calculation unit of the apparatus further includes:
the first vehicle following distance calculating subunit is used for acquiring the first position of the target vehicle and a third position of the first vehicle corresponding to the first position, and calculating the first sub vehicle following distance between the target vehicle and the first vehicle;
and the second following distance calculation subunit is used for acquiring the second position of the target vehicle and a fourth position of the first vehicle corresponding to the second position, and calculating a second sub following distance between the target vehicle and the first vehicle.
In other possible embodiments, the extraction unit 42 of the device further comprises:
and the speed calculation unit is used for calculating the running speed of the target vehicle based on the acquired time of the target vehicle reaching the plurality of preset positions.
As an example, the speed calculation unit of the apparatus further includes:
the first speed calculation subunit is used for calculating a first sub-running speed of the target vehicle based on the acquired time for the target vehicle to sequentially reach a first preset position and a second preset position;
the second speed calculation subunit is used for calculating a second sub-running speed of the target vehicle based on the acquired time for the target vehicle to sequentially reach the third preset position and the fourth preset position; the first preset position, the second preset position, the third preset position and the fourth preset position are a plurality of preset positions arranged along the driving direction.
In still other possible embodiments, the extraction unit 42 of the device further comprises:
and the license plate acquisition unit is used for acquiring the license plate state of the target vehicle, wherein the license plate state is a normal license plate, an unlinked license plate or a shielded license plate.
In still other possible embodiments, the extraction unit 42 of the device further comprises:
and the driving behavior detection unit is used for extracting the driving behavior characteristics of the target vehicle on the basis of the acquired plurality of image information of the target vehicle in the process of passing through the lane blocking device.
In some possible embodiments, the apparatus further comprises:
and the second judgment unit is used for acquiring the vehicle information of the actual passing vehicle, matching the vehicle information of the actual passing vehicle with the paid vehicle information, and taking the actual passing vehicle as a target vehicle when the matching fails.
It should be noted that, because the contents of information interaction, execution process, and the like between the above-mentioned apparatuses/units are based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof can be referred to specifically in the method embodiment section, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 5 is a schematic structural diagram of a device for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention. As shown in fig. 5, the vehicle intentional fee evasion detecting device of this embodiment includes: at least one processor 50 (only one shown in fig. 5), a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, the steps in any of the various vehicle intentional fare evasion detection method embodiments described above being implemented by the processor 50 when the computer program 52 is executed.
The vehicle intentional fee evasion detection device 5 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The vehicle intentional fee evasion detection device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of the vehicle intentional fee evasion detection device 5, and does not constitute a limitation of the vehicle intentional fee evasion detection device 5, and may include more or less components than those shown, or some components in combination, or different components, such as an input-output device, a network access device, etc.
The Processor 50 may be a Central Processing Unit (CPU), and the Processor 50 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may in some embodiments be an internal storage unit of the vehicle intentional fare evasion detection device 5, such as a hard disk or a memory of the vehicle intentional fare evasion detection device 5. The memory 51 may be an external storage device of the vehicle intentional fare evasion detection apparatus 5 in other embodiments, such as a plug-in hard disk provided on the vehicle intentional fare evasion detection apparatus 5, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 51 may also include both an internal storage unit of the vehicle intentional fee evasion detecting device 5 and an external storage device. The memory 51 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 51 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the invention also provides a vehicle intentional fee evasion detection device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above method embodiments.
Embodiments of the present invention provide a computer program product, which, when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which is stored in a computer readable storage medium and used for instructing related hardware to implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), random-access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for detecting intentional fee evasion of a vehicle, comprising:
acquiring vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle in a driving direction, and the vehicle information comprises at least one of image information, position information and time information;
extracting target vehicle characteristics based on the acquired vehicle information of the target vehicle and the first vehicle;
inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has intentional fee evasion behaviors or not by the detection machine learning model.
2. The method of claim 1, wherein the target vehicle characteristic comprises a license plate status characteristic and/or a body status characteristic, respectively,
the extracting of the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes:
and extracting the license plate state characteristics and/or the vehicle body state characteristics of the target vehicle based on the acquired image information of the target vehicle.
3. The vehicle intentional fee evasion detection method according to claim 1, wherein the target vehicle characteristic includes a following distance characteristic and a driving behavior characteristic, respectively,
the extracting of the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes:
calculating a following distance characteristic of a target vehicle and a first vehicle based on the acquired position information of the target vehicle and the position information of the first vehicle, wherein the target vehicle and the first vehicle are adjacent vehicles;
and extracting the driving behavior characteristics of the target vehicle based on the acquired position information of the target vehicle in the process of passing through the lane intercepting device.
4. The vehicle intentional fare evasion detection method according to claim 1, wherein the target vehicle characteristic includes a travel speed characteristic, and accordingly,
the extracting of the target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes:
and calculating the running speed of the target vehicle based on the acquired time of the target vehicle reaching a plurality of preset positions.
5. The vehicle intentional fee evasion detection method according to claim 1,
the inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, before the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors, further includes:
the method comprises the steps of obtaining vehicle information of an actual passing vehicle, matching the vehicle information of the actual passing vehicle with vehicle information paid for, and taking the actual passing vehicle as a target vehicle when matching fails.
6. A vehicle intentional fee evasion detection device, comprising:
an acquisition unit configured to acquire vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a preceding vehicle of the target vehicle in a traveling direction;
an extraction unit configured to extract a target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle;
and the judging unit is used for inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors or not.
7. A vehicle intentional fee evasion detection system, comprising: the vehicle position detection device, the license plate recognition device, the interception device and the data processing module are respectively connected with the data processing module, wherein,
the vehicle position detection device is used for detecting the positions of a first vehicle and a target vehicle and the driving behavior of the target vehicle in the process of passing through the intercepting device, and transmitting the positions of the first vehicle and the target vehicle and the driving behavior to the data processing module;
the license plate recognition device is used for detecting the license plate state of the target vehicle and transmitting the license plate state to the data processing module;
the data processing module is used for inputting the positions of the first vehicle and the target vehicle, the driving behavior and the license plate state into a pre-trained intentional fee evasion detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee evasion behavior.
8. The vehicle intentional fee evasion detection system of claim 7, further comprising: a charging module connected to the data processing module, wherein,
the vehicle position detection device is also used for acquiring vehicle information of an actual passing vehicle and transmitting the vehicle information of the actual passing vehicle to the data processing module;
the charging module is used for transmitting the paid vehicle information to the data processing module;
and the data processing module is used for receiving the actual passing vehicle and the paid vehicle information and carrying out matching analysis on the vehicle information of the actual passing vehicle and the paid vehicle information.
9. The vehicle intentional fee evasion detection system according to claim 7, further comprising a bracket on which the vehicle position detection device and a communication antenna module for communication connection with the target vehicle are disposed, the communication antenna module being connected with the toll collection module.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 5.
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| CN202110053514.3A CN112750220A (en) | 2021-01-15 | 2021-01-15 | Method, device and system for detecting intentional fee evasion of vehicle and storage medium |
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| CN202110053514.3A CN112750220A (en) | 2021-01-15 | 2021-01-15 | Method, device and system for detecting intentional fee evasion of vehicle and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113838221A (en) * | 2021-09-22 | 2021-12-24 | 招商华软信息有限公司 | Fee chasing method and device, storage medium and computer equipment |
| CN115273259A (en) * | 2022-07-21 | 2022-11-01 | 北京物资学院 | Vehicle identification method, device, equipment and medium |
| CN116630903A (en) * | 2022-12-29 | 2023-08-22 | 北京中科神通科技有限公司 | Method and system for detecting behavior fee evasion of highway counterfeit bus |
| CN116778727A (en) * | 2023-08-10 | 2023-09-19 | 太极计算机股份有限公司 | Control method of anti-rubbing ETC system |
| CN117373259A (en) * | 2023-12-07 | 2024-01-09 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
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- 2021-01-15 CN CN202110053514.3A patent/CN112750220A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113838221A (en) * | 2021-09-22 | 2021-12-24 | 招商华软信息有限公司 | Fee chasing method and device, storage medium and computer equipment |
| CN115273259A (en) * | 2022-07-21 | 2022-11-01 | 北京物资学院 | Vehicle identification method, device, equipment and medium |
| CN116630903A (en) * | 2022-12-29 | 2023-08-22 | 北京中科神通科技有限公司 | Method and system for detecting behavior fee evasion of highway counterfeit bus |
| CN116630903B (en) * | 2022-12-29 | 2024-03-08 | 北京中科神通科技有限公司 | Method and system for detecting behavior fee evasion of highway counterfeit bus |
| CN116778727A (en) * | 2023-08-10 | 2023-09-19 | 太极计算机股份有限公司 | Control method of anti-rubbing ETC system |
| CN116778727B (en) * | 2023-08-10 | 2023-12-19 | 太极计算机股份有限公司 | Control method of anti-rubbing ETC system |
| CN117373259A (en) * | 2023-12-07 | 2024-01-09 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
| CN117373259B (en) * | 2023-12-07 | 2024-03-01 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
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