CN112750220B - A method, device, system and storage medium for detecting intentional fare evasion by vehicles - Google Patents
A method, device, system and storage medium for detecting intentional fare evasion by vehicles Download PDFInfo
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- G—PHYSICS
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- 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
本发明提供了一种车辆故意逃费检测方法、装置、系统及存储介质，包括：获取目标车辆和第一车辆的车辆信息，其中，所述第一车辆为所述目标车辆沿行驶方向的前车车辆，所述车辆信息包括图像信息、位置信息和时间信息中的至少一种；基于获取的所述目标车辆和第一车辆的车辆信息，提取目标车辆特征；将所述目标车辆特征输入至预训练的故意逃费检测机器学习模型中，所述检测机器学习模型判断所述目标车辆是否存在故意逃费行为。通过此方法可以有效区分无意逃费和故意逃费车辆，以及时对故意逃费的车辆采取相应措施。
The present invention provides a method, device, system and storage medium for detecting intentional fare evasion by vehicles, including: obtaining vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is the preceding vehicle of the target vehicle in the driving direction, and the vehicle information includes at least one of image information, position information and time information; extracting target vehicle features based on the obtained vehicle information of the target vehicle and the first vehicle; inputting the target vehicle features into a pre-trained intentional fare evasion detection machine learning model, and the detection machine learning model determines whether the target vehicle has intentional fare evasion behavior. This method can effectively distinguish between vehicles that do not intend to evade fees and vehicles that do intend to evade fees, and take corresponding measures against vehicles that do intend to evade fees in a timely manner.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and particularly relates to a method, a device, a system and a storage medium for detecting intentional fee evasion of a vehicle.
Background
Along with the continuous increase of the insurance of motor vehicles in China, the traditional manual toll collection mode can not meet the current rapid passing requirement, more and more vehicle owners select an electronic toll collection system (Electronic Toll Collection, abbreviated as ETC) lane at high speed to rapidly pay and pass, and in urban traffic, an intelligent parking lot is combined with various payment platforms to also push out an unattended parking toll collection mode, so that passing efficiency is improved, and labor cost is saved.
The system belongs to an unattended toll lane regardless of a high-speed ETC or an intelligent parking lot, vehicles passing through without paying toll often appear in the operation process, and the vehicles belong to toll vehicles, but in the toll vehicles, some vehicles escape due to system abnormality or other non-subjective factors, and other vehicles use holes existing in a toll system to intentionally escape.
According to the unpaid record, the unintentional fee evasion and the intentional fee evasion can be judged as the same type of fee evasion vehicle, and the unintentional fee evasion and the intentional fee evasion vehicle cannot be distinguished.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a system and a storage medium for detecting intentional fee evasion of a vehicle, which can solve the problem that unintentional fee evasion and intentional fee evasion of the vehicle cannot be distinguished.
In a first aspect, an embodiment of the present invention provides a method for detecting intentional fee evasion of a vehicle, including:
The method comprises the steps of obtaining vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle along a running direction, extracting target vehicle characteristics based on the obtained 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 intentional fee evasion behaviors exist in the target vehicle by the detection machine learning model.
Optionally, the target vehicle feature includes a license plate status feature and/or a body status feature. Correspondingly, the extracting the characteristics of the target vehicle based on the acquired vehicle information of the target vehicle and the first vehicle comprises 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.
Optionally, the target vehicle characteristic includes a target vehicle characteristic and a driving behavior characteristic. Correspondingly, the extracting the characteristics of the target vehicle based on the acquired vehicle information of the target vehicle and the first vehicle comprises 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. And extracting the driving behavior characteristics of the target vehicle based on the acquired position information in the process that the target vehicle passes through the lane interception device.
Optionally, the target vehicle characteristic includes a travel speed characteristic. Correspondingly, the method for extracting the characteristics of the target vehicle based on the acquired vehicle information of the target vehicle and the first vehicle comprises the step of calculating the running speed of the target vehicle based on the acquired time when the target vehicle reaches a plurality of preset positions.
Optionally, the method includes inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and before the detection machine learning model judges whether the target vehicle has intentional fee evasion behavior, acquiring vehicle information of an actual passing vehicle, matching the vehicle information of the actual passing vehicle with paid vehicle information, and taking the actual passing vehicle as the target vehicle when matching fails.
In a second aspect, an embodiment of the present invention provides a vehicle intentional fee evasion detection device, which includes an acquisition unit configured to acquire vehicle information of a target vehicle and a first vehicle, where 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 a determination unit configured to input the target vehicle feature into a pre-trained intentional fee evasion detection machine learning model, where the detection machine learning model determines whether an intentional fee evasion behavior exists in the target vehicle.
In a third aspect, the embodiment of the invention provides a vehicle intentional fee escaping detection system, which 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 positions of a first vehicle and a target vehicle and running behaviors 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 running behaviors to the data processing module, the license plate recognition device is used for detecting license plate states of the target vehicle and transmitting the license plate states to the data processing module, and the data processing module is used for inputting the positions, the running behaviors and the license plate states of the first vehicle and the target vehicle into a pre-trained intentional fee escaping detection machine learning model, and the detection machine learning model judges whether the target vehicle has intentional fee escaping behaviors or not.
The system comprises a data processing module, a charging module, a data processing module and a vehicle position detection device, wherein the data processing module is used for receiving the actual passing vehicles and the paid vehicle information and carrying out matching analysis on the actual passing vehicles and the paid vehicle information.
Optionally, the vehicle position detection device and the communication antenna module for communication connection with the target vehicle are arranged on the bracket, and the communication antenna module is connected with the charging module.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, which when run on a terminal device, causes the terminal device to perform the vehicle intentional fare evasion detection method of any of the above first aspects.
It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.
The method comprises the steps of calculating the following distance between a target vehicle and a first vehicle through the obtained position of the target vehicle and the position of the first vehicle, calculating the running speed of the target vehicle based on the obtained time when the target vehicle reaches a plurality of preset positions, obtaining the license plate state of the target vehicle, wherein the specific license plate state can be a normal license plate, an unhuspension license plate or a shielded license plate, obtaining the running behavior of the target vehicle in the process of passing through a lane interception device, wherein the specific running behavior comprises normal, parking or reversing behavior, inputting the obtained information into a pre-trained intentional fee escaping detection machine learning model, judging whether the intentional fee escaping behavior exists in the target vehicle through the detection machine learning model, further judging whether the target vehicle is the intentional fee escaping vehicle, and timely taking corresponding measures for the intentional fee escaping vehicle.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for detecting intentional fee evasion of a vehicle according to an embodiment of the present invention;
FIG. 3 is a 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 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 the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present 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 the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the 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 invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified 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 escaping means at present in the process of researching intentional fee escaping and unintentional fee escaping, firstly, a falling pole coil in a toll lane is utilized to have a certain width, a target vehicle is enabled to misjudge that two vehicles are the same vehicle through a vehicle which normally passes in the immediate front, and secondly, the function of preventing the vehicle from smashing of an interception device is triggered to enable the interception device to be forcedly lifted, so that the purpose of fee escaping and passing is achieved.
The method is characterized in that a grating is adopted to replace a coil, a high-speed interception device is adopted to increase the difficulty of fee escaping, the method cannot completely stop the intentional fee escaping action, and the charging record running water is compared through other vehicle fee escaping detection equipment to locate an unpaid vehicle and realize the later-stage fee escaping through later-stage fee tracing.
The vehicle with the intentional fee evasion belongs to the malicious behavior, and a certain punishment can be given if necessary. For a vehicle that intentionally escapes, there are typically a large number of escape records, the rules of which can be analyzed by the records to determine the vehicle that intentionally escapes.
The vehicle intentional fee evasion detection 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 can be used for detecting positions of a first vehicle and a target vehicle and running behaviors 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 running behaviors to the data processing module, and the specific vehicle position detection device can be set to be used for detecting positions of a laser sensor, an ultrasonic sensor, a video detector and the like. The license plate recognition device can extract and recognize the license plate state of the moving target automobile from a complex background, can be used for detecting the license plate state of the target automobile and transmitting 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 state of the first automobile and the target automobile into a pre-trained intentional fee escaping detection machine learning model and judging whether the target automobile has intentional fee escaping behaviors or not through the detection machine learning model.
According to the embodiment of the invention, the vehicle position detection device detects the positions and the running 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 escaping detection machine learning model through the data processing module, so that the fee escaping vehicle can be judged to be unintentional fee escaping or intentional fee escaping.
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 system for detecting intentional fee evasion of a vehicle in an ETC lane, including a bracket 8, an interception device 7, a vehicle position detection device 1, a license plate recognition device 2, a charging module 4 such as an ETC charging system, a communication antenna module 3 such as an ETC antenna and a data processing module 5, and the vehicle position detection device 1, the license plate recognition device 2, the charging 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 may specifically detect the position by a laser sensor, an ultrasonic sensor, a video detector, or the like. For example, when the laser sensor detection position is set, a plurality of laser transmitters and a plurality of laser receivers may be set, and when the target vehicle is, laser irradiation is blocked, and the time for the target vehicle to pass this laser transmitter may be acquired. In addition, in this embodiment, the laser sensor is further configured to count the vehicles passing through, and the payment record number transmitted by the charging module 4 is compared with the actually detected passing data, so that the vehicles passing through without payment can be obtained, and then the data passing through without payment is input into the trained intentional fee escaping detection machine learning model, so as to determine whether the target vehicle intentionally escapes.
The license plate recognition device 2 may be a license plate recognizer for recognizing the license plate number of the vehicle and detecting the actual suspension state of the license plate. Of course, the license plate recognition device 2 may also be configured as a photographing device for photographing the license plate state and detecting the actual suspension state of the license plate, and may be matched with a low-power road side unit for acquiring the unique MAC code or electronic license plate of the vehicle-mounted unit of the vehicle, or may be other devices capable of acquiring the identification information of the vehicle, such as RFID, or may be used for recognizing the electronic license plate of the vehicle, and detecting the electronic license plate may be used as a subsequent additional payment basis.
The blocking device 7 may be a railing machine, a drawbar machine, a barrier gate machine, an electric telescopic door, etc., or other devices that may be used to block the passage of vehicles, and is not limited in this embodiment.
Specifically, the vehicle position detecting device 1 and the communication antenna module 3 are disposed on the top of the bracket 8 in this embodiment, so as to increase the detection range, and the scanning direction of the vehicle position detecting device 1 may be scanning along the driving direction of the target vehicle 9, so as to detect the positions of the target vehicle 9 and the first vehicle 10. The blocking device 7 may be disposed at a rear position of the support 8, and a certain interval, for example, 3m, may be disposed between the blocking device 7 and the support 8. The bracket 8 and the charging 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 protect the toll collection module 4 and the data processing module 5, both may be provided in a kiosk 6.
In other possible embodiments, referring to fig. 2, fig. 2 shows a schematic structural diagram of a system for detecting intentional fare evasion of a vehicle in an intelligent parking lot, which includes a vehicle position detecting device 1, a license plate identifying 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 identifying device 2, the charging module 4 and the intercepting device 3 are respectively connected with the data processing module 5, and the connection mode may be a communication connection, for example, a wired communication connection, for example HDMI, USB, RS, RS232, etc., or a wireless communication connection, for example, a bluetooth or WiFi, etc., and for example, the vehicle position detecting device 1, the license plate identifying device 2, the data processing module 5 and the charging module 4 may be all connected on the same switch through network interfaces.
Specifically, the vehicle position detecting device 1 and the license plate recognition device 2 can be arranged on the same side of a lane, the interception device 3 can be arranged at a position about 1 meter behind the vehicle position detecting device 1, and the license plate recognition device 2 can be arranged at a position about 3 meters behind the interception device 3. To protect the toll collection module 4 and the data processing module 5, both may be provided in a kiosk 6.
Fig. 3 is a schematic flow chart of a method for detecting intentional fare evasion of a vehicle according to an embodiment of the present invention, specifically:
s100, acquiring vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle along a running 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 sends the detected position information to a data processing module for analysis processing. The position can be detected specifically by a laser sensor, an ultrasonic sensor, a video detector, or the like. And may record the time at which the vehicle position detection device detected the position. For example, when the laser sensor detection position is set, a plurality of laser transmitters and a plurality of laser receivers may be set, and when the target vehicle or the first vehicle passes, the laser irradiation is blocked, and the time for the target vehicle to pass this laser transmitter may be acquired. The present embodiment can acquire image information of a vehicle, such as photographing equipment, image capturing equipment, and the like, by an image acquisition device.
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 features include following distance features and driving behavior features, and accordingly, 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, a distance between the head position of the target vehicle and the tail position of the first vehicle is taken as a following distance, the following distance is taken as a following distance feature and is input into the machine learning model, and in this case, the vehicle distance between the target vehicle and the first vehicle is relatively smaller and more consistent with the scanning range of the laser sensor.
In one possible implementation, step S210 specifically includes:
S211, acquiring a first position of a target vehicle and a third position of the first vehicle corresponding to the first position, and calculating a first sub-following distance between the target vehicle and the first vehicle;
s212, acquiring a 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.
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 following distance and a second sub following distance, and taking the first sub following distance and the second sub following distance as input data of a pre-trained intentional fee escaping 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 calculated.
As an example and not by way of limitation, the position of the interception device is selected as the origin, the front Y1 and the front Y2 of the interception device are set as row measuring points, Y1 and Y2 are row measuring points at different positions, for example Y1 is 5m, Y2 is 2m, and the scanning range of the laser sensor is-10 m to 10m. 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 front 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 front vehicle is detected at the same time, and when the head of the target vehicle is respectively at Y1 and Y2, the following distances between the head of the target vehicle and the front vehicle are respectively d1=y1-Y3 and d2=y2-Y4. It can be understood that when the 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 head of the target vehicle reaches the position Y1 or Y2, for example, the first vehicle is not in a scanning range set by the laser sensor, for example, -10m to 10m, the position of the tail of the first vehicle may be valued to be-10 m, and, for example, the first vehicle is not in a scanning range set by the laser sensor, for example, -5m to 5m, the position of the tail of the first vehicle may be valued to be-5 m, and the distance between the head of the target vehicle and the tail of the first vehicle may be calculated.
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. The driving behavior is input as driving behavior characteristics into the machine learning model.
When the head of the target vehicle reaches the position of the interception device, the vehicle position detection device is used as a time period of the process that the target vehicle passes through the lane interception device from the time when the tail of the target vehicle completely passes through the position of the interception device, and detects the running behavior of the target vehicle in the time period.
In other embodiments, the target vehicle feature may be extracted based on the acquired time information.
The target vehicle characteristics include a travel speed characteristic, and accordingly, step S200 may include:
s230, calculating a traveling speed of the target vehicle based on the acquired times when the target vehicle reaches the plurality of preset positions.
The vehicle position detection device may be employed to detect when the target vehicle reaches a preset position and send the time to the data processing module, and then input the travel speed as a travel speed feature into the machine learning model.
In particular, the driving speeds may include a first sub-driving speed and a second sub-driving speed, and, correspondingly, step S230 includes,
S231, calculating a first sub-running speed of the target vehicle based on the acquired time when the target vehicle sequentially reaches the first preset position and the second preset position;
S232, calculating a second sub-running speed of the target vehicle based on the acquired time when the target vehicle sequentially reaches a third preset position and a fourth preset position, wherein 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 running direction.
When the target vehicle is located in different position segments, the running speed of the target vehicle, namely a first sub-running speed and a second sub-running speed, is calculated, and the first sub-running speed and the second sub-running speed are used as input data of a pre-trained intentional fee escaping detection machine learning model.
By way of example and not limitation, the location of the interceptor is chosen as the origin, and X1, X2, X3, X4 in front of the interceptor are points to be measured at four different locations, e.g. X1 is 6m, X2 is 5m, X3 is 2m, X4 is 1m, further e.g. X1 is 5m, X2 is 4m, X3 is 1.5m, X4 is 0.5m. When the vehicle position detecting means detects that the head of the target vehicle reaches the four positions described above, the time at that time is recorded as T1, T2, T3, and T4, respectively, and the travel speed of the target vehicle between Y1 and Y2 is calculated as v1= (X1-X2)/(T2-T1), and the travel 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 license plate status characteristics and/or body status characteristics, and travel speed characteristics, and accordingly, step S200 may include:
S240, extracting license plate state features and/or vehicle body state features of the target vehicle based on the acquired image information of the target vehicle;
As an illustration, license plate status features including normal license plates, unhooked license plates, and blocked license plates, S may be used to represent license plate status. For example, a position M, such as 5M or 3M, in front of the license plate recognition device is selected as a snapshot point, and after 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 the vehicle body forms of a car, a passenger car and a truck, the fee escaping probabilities of different types of vehicles are different, and the fee escaping probability of the truck is higher due to higher passing fee, so that the accuracy of judgment can be improved by taking the vehicle body state as an input characteristic.
S300, inputting the characteristics of the target vehicle into a pre-trained intentional fee evasion detection machine learning model, and judging whether the target vehicle has intentional fee evasion behaviors by the detection machine learning model. The method can effectively distinguish unintentional fee evasion and intentional fee evasion vehicles.
In some possible embodiments, the trigger state information of the anti-smashing function of the gateway can be input into the training model by acquiring the trigger state information, so that the accuracy of the judging result is improved.
In other possible embodiments, the accuracy of the determination result may also be improved by detecting the status information of the on-board unit (Onboard Unit, OBU) of the target vehicle, where the status information may include a detached status, an un-plugged status, a no-entry status, and the like, and inputting the status information into the training model.
It should be noted that the pre-trained intentional fee evasion detection machine learning model is trained by the following steps:
s410, extracting vehicle features of the fee evasion data samples, and constructing feature vectors;
it can be understood that the feature vector can be constructed according to the first sub following distance D1 and the second sub following distance D2, the first sub driving speed V1 and the second sub driving following distance V2, the license plate state S and the driving behavior O of the target vehicle;
S420, training the fee evasion data sample to obtain a pre-trained intentional fee evasion detection machine learning model.
The fee evasion data sample can be trained by adopting a classification algorithm based on the combination of a neural network and a decision tree, and other algorithms can be used for training, such as a regression algorithm, a Bayesian method and the like. The method comprises the steps of judging whether an ETC system is in fault or not according to a preset condition, judging whether the ETC system is in fault or not according to the preset condition, wherein the charge escaping sample data are used for distinguishing unintentional charge escaping and intentional charge escaping main general manual checking, for example, a single vehicle frequently appears in the charge escaping data, or a vehicle with obvious intentional charge escaping behavior can be used as an intentional charge escaping sample through manual observation, the charge escaping sample appearing when the ETC system is in fault can be used as an unintentional charge escaping sample, and the sample which cannot be manually judged whether the intentional charge escaping is in the fault or not can be temporarily removed and is not put into a training sample, so that the accuracy of a final training model is improved.
According to the embodiment of the invention, the target vehicle characteristics of the target vehicle are acquired, the acquired information is input into the pre-trained intentional fee escaping detection machine learning model, whether the target vehicle has intentional fee escaping behavior or not can be judged through the detection machine learning model, whether the target vehicle is an intentional fee escaping vehicle or not is further judged, corresponding measures are timely taken for the intentional fee escaping vehicle, and economic losses are reduced.
In one possible implementation manner, before step S300, the method further includes:
s500, acquiring vehicle information of the actual passing vehicle, matching the vehicle information of the actual passing vehicle with paid vehicle information, and taking the actual passing vehicle as a target vehicle when the matching fails.
When the matching fails, the fact that the actual passing vehicle is not paid is indicated, the vehicle is used as a target vehicle, the vehicle information of the target vehicle is input into a pre-trained intentional fee escaping detection machine learning model, the machine learning model is detected to judge whether the target vehicle has intentional fee escaping behaviors, and the vehicle can be judged to be unintentional fee escaping or intentional fee escaping. And when the matching is successful, the fact that the actual passing vehicles are paid is indicated as normal passing vehicles. The vehicle information comprises a following distance, a driving speed, a license plate state and driving behaviors.
As an example, the laser sensor may be used to count the actual passing vehicles, and the number of payment records recorded by the ETC charging system or the parking lot charging system is compared with the actually detected passing data, so that the vehicles of the unpaid passing can be obtained, and then the vehicle information of the target vehicles of the unpaid passing is input into the pre-trained intentional fee escaping detection machine learning model, so as to determine whether the target vehicles intentionally escape.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Corresponding to the vehicle intentional fee evasion detection method of the above embodiments, fig. 4 shows a block diagram of the vehicle intentional fee evasion detection apparatus provided by the embodiment of the present invention, and for convenience of explanation, only the portion related to the embodiment of the present invention is shown.
Referring to fig. 4, the apparatus includes:
an acquiring 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 for extracting a target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle;
A first judging unit 43 for inputting the characteristics of the target vehicle into a pre-trained intentional fee evasion detection machine learning model, the detection machine learning model judging whether the target vehicle has intentional fee evasion behavior.
In some possible embodiments, the extraction unit 42 of the device further comprises:
the following distance calculating unit is used for calculating 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 calculating unit of the apparatus further includes:
the first following distance calculating subunit is used for 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 following distance between the target vehicle and the first vehicle;
And the second following distance calculating subunit is used for acquiring the second position of the target vehicle and the fourth position of the first vehicle corresponding to the second position and calculating the second following distance between the target vehicle and the first vehicle.
In other possible embodiments, the extraction unit 42 of the device further comprises:
And a speed calculation unit for calculating a running speed of the target vehicle based on the obtained times when the target vehicle reaches the plurality of preset positions.
As an example, the speed calculation unit of the apparatus further includes:
A first speed calculating subunit, configured to calculate a first sub-running speed of the target vehicle based on the obtained time when the target vehicle sequentially reaches the first preset position and the second preset position;
The second speed calculating subunit is used for calculating a second sub-running speed of the target vehicle based on the time when the acquired target vehicle sequentially reaches a third preset position and a fourth preset position, wherein 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 running direction.
In still other possible embodiments, the extraction unit 42 of the device further comprises:
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 unhatched license plate or a blocked 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 driving behavior characteristics of the target vehicle based on the acquired multiple image information in the process that the target vehicle passes through the lane interception device.
In some possible embodiments, the apparatus further comprises:
And the second judging 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 the target vehicle when the matching fails.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is 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 detection apparatus of this embodiment includes at least one processor 50 (only one is 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 processor 50 implementing the steps in any of the respective vehicle intentional fee evasion detection method embodiments described above when executing the computer program 52.
The vehicle intentional fee evasion detection device 5 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The vehicle intentional fee evasion detection apparatus may include, but is not limited to, a processor 50, a memory 51. It will be appreciated by those skilled in the art that fig. 5 is merely an example of the vehicle intentional fee evasion detection apparatus 5, and does not constitute a limitation of the vehicle intentional fee evasion detection apparatus 5, and may include more or less components than those illustrated, or may combine some components, or different components, for example, may further include an input-output device, a network access device, and the like.
The Processor 50 may be a central processing unit (Central Processing Unit, CPU), the Processor 50 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. 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 memory of the vehicle intentional fare evasion detection device 5. The memory 51 may also be an external storage device of the vehicle intentional fee evasion detection apparatus 5 in other embodiments, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the vehicle intentional fee evasion detection apparatus 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the vehicle intentional fee evasion detection apparatus 5. The memory 51 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, etc., such as program codes of computer programs, etc. 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 which is stored in the memory and can be run on the at least one processor, wherein the processor executes the computer program to realize the steps in any of the method embodiments.
The embodiments of the present invention also provide a computer readable storage medium storing a computer program, which when executed by a processor implements steps of the above-described respective method embodiments.
Embodiments of the present invention provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above-described embodiments, and may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include at least any entity or device capable of carrying computer program code to a camera device/terminal equipment, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
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 solution. 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 manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions described in the foregoing embodiments may be modified or some of the technical features may be equivalently replaced, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and are included in the protection scope of the present invention.
Claims (9)
1. A vehicle intentional fee evasion detection method for an unattended toll lane, the vehicle intentional fee evasion detection method comprising:
Acquiring vehicle information of a target vehicle and a first vehicle, wherein the first vehicle is a front vehicle of the target vehicle along a running direction, and the vehicle information comprises at least one of image information, position information and time information;
extracting a target vehicle characteristic 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, wherein the detection machine learning model judges whether the target vehicle has intentional fee evasion behaviors;
the target vehicle characteristics include following distance characteristics and driving behavior characteristics, and, correspondingly,
The extracting a 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;
extracting 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 interception device;
when the head of the target vehicle reaches the position of the interception device, the vehicle position detection device is used for detecting the running behavior of the target vehicle in a time period as the process that the target vehicle passes through the lane interception device until the tail of the target vehicle completely passes through the position of the interception device.
2. The method for detecting intentional fee evasion of a vehicle according to claim 1, wherein the target vehicle features include license plate status features and/or body status features, respectively,
The extracting a target vehicle feature based on the acquired vehicle information of the target vehicle and the first vehicle includes:
and extracting license plate state features and/or vehicle body state features of the target vehicle based on the acquired image information of the target vehicle.
3. The method for detecting intentional fee evasion of a vehicle according to claim 1, wherein the target vehicle characteristic includes a travel speed characteristic, and correspondingly,
The extracting a 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 when the target vehicle reaches a plurality of preset positions.
4. The method for detecting intentional fee evasion of a vehicle according to claim 1, wherein,
The method includes inputting the target vehicle characteristics into a pre-trained intentional fee evasion detection machine learning model, and before the detection machine learning model judges whether the target vehicle has intentional fee evasion behavior, further comprising:
And 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.
5. A vehicle intentional fee evasion detection apparatus applying the vehicle intentional fee evasion detection method according to claim 1, characterized by 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 characteristics of 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.
6. A vehicle intentional fee evasion detection system is characterized in that the vehicle intentional fee evasion detection system is used for an unattended toll lane and 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 the first vehicle and the target vehicle and the running 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 running 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 behaviors and the license plate states 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;
When the head of the target vehicle reaches the position of the interception device, the vehicle position detection device detects the running behavior of the target vehicle in the time period as the time period of the process that the target vehicle passes through the interception device until the tail of the target vehicle completely passes through the position of the interception device.
7. The vehicle intentional fee evasion detection system of claim 6 further comprising a billing module coupled to the data processing module, wherein,
The vehicle position detection device is also used for acquiring the vehicle information of the actual passing vehicles and transmitting the vehicle information of the actual passing vehicles to the data processing module;
the charging module is used for transmitting the paid vehicle information to the data processing module;
The data processing module is used for receiving the actual passing vehicles and the paid vehicle information and carrying out matching analysis on the vehicle information of the actual passing vehicles and the paid vehicle information.
8. The vehicle intentional fee evasion detection system of 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 module.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 4.
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| CN115273259B (en) * | 2022-07-21 | 2023-07-28 | 北京物资学院 | Vehicle identification method, device, equipment and medium |
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| 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 |
| CN119068565A (en) * | 2024-08-06 | 2024-12-03 | 陕西华贝金服网络科技有限公司 | Parking lot intelligent anti-escape system and method and computer equipment |
| CN119691414A (en) * | 2025-02-25 | 2025-03-25 | 安徽驿路微行科技有限公司 | Fee evasion behavior checking method, device, computer equipment and readable storage medium |
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