Intelligent inspection tour payment method and device based on ETC (Electronic Toll Collection) and RFID (Radio Frequency Identification)

Abstract

The invention discloses an ETC and RFID-based intelligent inspection payment method and device, and the method comprises the steps: screening unpaid orders meeting conditions through a designated parking cloud platform, obtaining arrearage orders, and pushing the arrearage orders to an ETC middleware cloud platform in real time; immediately synchronizing to an edge server of the ETC inspection vehicle for storage; the ETC inspection vehicle drives and inspects according to a preset route through the unmanned control system; road images are collected in real time through an AI recognition system and processed, and vehicle information of a recognized vehicle is obtained; parking space matching is carried out through the RFID technology, and the position of the identified vehicle parking space is confirmed; uploading the vehicle information and the road image to a cloud platform for processing to obtain charging information; and the ETC inspection vehicle carries out ETC fee deduction processing on the identification vehicle equipped with the ETC according to the charging information and the arrearage order and issues fee deduction success information. According to the invention, the road test vehicle can be automatically identified, ETC road test recovery is supported, efficient and accurate automatic charging is realized, and the road traffic management and charging efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to an intelligent inspection and payment method and device based on ETC and RFID. Background Art

[0002] With the rapid development of intelligent transportation systems, higher requirements are placed on the efficient management and automatic toll collection of road vehicles.

[0003] At present, the traditional manual inspection and toll collection methods have problems such as low efficiency, high cost, and easy errors. Although some existing automatic toll collection systems have improved efficiency to a certain extent, they still have problems such as low recognition accuracy and inability to effectively pursue vehicles that evade tolls.

[0004] Therefore, it is particularly important to develop an unmanned electric inspection vehicle system that can automatically identify vehicles, support ETC road testing and collection, and automatic toll collection. Summary of the invention

[0005] To this end, the present invention provides an intelligent inspection and payment method and device based on ETC and RFID, which can automatically identify road test vehicles, support ETC road test and collection, realize efficient and accurate automatic charging, and improve road traffic management and charging efficiency.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an intelligent inspection and payment method based on ETC and RFID, comprising:

[0007] Screening qualified unpaid orders through a designated parking cloud platform to obtain arrears orders; pushing the arrears orders to the ETC middleware cloud platform in real time through the designated parking cloud platform;

[0008] After receiving the overdue payment order, the ETC middleware cloud platform immediately synchronizes it to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received overdue payment order;

[0009] The ETC inspection vehicle uses an unmanned driving control system to conduct inspections along a preset route;

[0010] The ETC inspection vehicle collects and processes road images in real time through the AI ​​recognition system to obtain vehicle information for identifying vehicles;

[0011] The ETC inspection vehicle uses RFID technology to match parking spaces and confirm the location of the parking space where the identified vehicle is parked;

[0012] The ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle;

[0013] The ETC inspection vehicle performs ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; if the identified vehicle is not equipped with ETC, the road test recovery process is initiated;

[0014] The ETC middleware cloud platform sends down the deduction success information and saves it in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle.

[0015] As a preferred solution for the intelligent inspection and payment method based on ETC and RFID, when the ETC inspection vehicle uses an unmanned driving control system to inspect along a preset route, the decision-making and path planning steps of the ETC inspection vehicle are as follows:

[0016] The ETC inspection vehicle receives the task instructions from the designated parking cloud platform;

[0017] Collect environmental information around the vehicle in real time through on-board sensors;

[0018] Through GPS positioning and map matching technology, determine the corresponding relationship between the current position of the ETC inspection vehicle and the map;

[0019] Predict and analyze potential dangers and traffic flow based on collected environmental information;

[0020] Make behavioral decisions and route planning for ETC inspection vehicles based on mission instructions and environmental analysis results;

[0021] By setting the smoothing strategy and optimization strategy, the generated path is smoothed and optimized;

[0022] converting the formulated behavioral decisions into movement control instructions;

[0023] The ETC inspection vehicle drives according to the motion control instructions and performs the predetermined inspection tasks;

[0024] During the execution of the task, the on-board sensors continuously monitor environmental changes and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and route planning based on the uploaded data and task execution status;

[0025] After the task is completed, the efficiency and effectiveness of the task execution are evaluated;

[0026] All data during the task execution will be recorded and uploaded to the designated parking cloud platform for subsequent analysis and optimization.

[0027] As a preferred solution for the intelligent inspection and payment method based on ETC and RFID, in the process of the ETC inspection vehicle collecting road images in real time through the AI ​​recognition system, the steps of environmental perception and data collection and processing are as follows:

[0028] The ETC inspection vehicle uses laser radar, cameras, millimeter-wave radar, and ultrasonic sensors to collect environmental information around the vehicle in real time and obtain original data.

[0029] The collected raw data is cropped, resized and denoised to obtain preprocessed sensor data;

[0030] Through the sensor fusion algorithm, the pre-processed sensor data is fused to obtain fused data;

[0031] constructing a three-dimensional environment model according to the fused data;

[0032] The fused data is processed by a computer vision algorithm to obtain vehicle information; the occupancy status and time of the parking space are monitored by geomagnetic equipment to obtain vehicle parking space occupancy information;

[0033] The vehicle information and the vehicle parking space occupancy information are uploaded to the designated parking cloud platform in real time.

[0034] As a preferred solution of the intelligent inspection and payment method based on ETC and RFID, in the process of processing the fused data by computer vision algorithm and obtaining the vehicle information, the steps of identification processing are:

[0035] Perform denoising and enhancement preprocessing on the captured vehicle images;

[0036] Extracting vehicle features of the identified vehicle through a deep learning network;

[0037] According to the extracted vehicle features, the vehicle is identified by a classifier and the vehicle information is output.

[0038] As a preferred solution of the intelligent inspection and payment method based on ETC and RFID, in the process of the ETC inspection vehicle matching the parking space through RFID technology and confirming the position of the identified vehicle parking space, the parking space matching confirmation steps are:

[0039] Installing unique RFID tags on each berth;

[0040] The RFID tag information on the berth is read in real time through the RFID reader on the ETC inspection vehicle;

[0041] The vehicle information of the identified vehicle is matched and confirmed with the berth information read by the RFID.

[0042] The present invention also provides an intelligent inspection and payment device based on ETC and RFID, which adopts the above-mentioned intelligent inspection and payment method based on ETC and RFID, including:

[0043] As a preferred solution of the intelligent inspection and payment device based on ETC and RFID, the overdue payment order acquisition and push unit is used to screen the qualified unpaid orders through the designated parking cloud platform to obtain the overdue payment orders; and push the overdue payment orders to the ETC middleware cloud platform in real time through the designated parking cloud platform;

[0044] The overdue payment order receiving and storage unit is used for the ETC middleware cloud platform to immediately synchronize the overdue payment order to the edge server of the ETC inspection vehicle after receiving the overdue payment order; the edge server of the ETC inspection vehicle stores the received overdue payment order;

[0045] The ETC inspection vehicle inspection unit is used for the ETC inspection vehicle to inspect along the preset route through the unmanned driving control system;

[0046] The vehicle information recognition unit is used by the ETC inspection vehicle to collect and process road images in real time through the AI ​​recognition system to obtain vehicle information for identifying the vehicle;

[0047] The parking space matching confirmation unit is used for the ETC inspection vehicle to match the parking space through RFID technology to confirm the position of the identified vehicle parking space;

[0048] The charging information acquisition unit is used for the ETC inspection vehicle to upload the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the charging information of the identified vehicle, and sends the charging information to the edge server of the ETC inspection vehicle;

[0049] The deduction processing unit is used for the ETC inspection vehicle to perform ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; if the identified vehicle is not equipped with ETC, the road test recovery process is started;

[0050] The deduction information sending unit is used for the ETC middleware cloud platform to send the deduction success information and save it in the obu of the on-board ETC of the identified vehicle, and send the deduction success information to the ETC app of the owner of the identified vehicle.

[0051] As a preferred solution of an intelligent inspection and payment device based on ETC and RFID, in the inspection unit of the ETC inspection vehicle, the decision-making and path planning module of the ETC inspection vehicle includes:

[0052] Task instruction receiving module, used for ETC inspection vehicles to receive task instructions issued by the designated parking cloud platform;

[0053] Environmental information collection module, used to collect environmental information around the vehicle in real time through on-board sensors;

[0054] The vehicle location positioning module is used to determine the corresponding relationship between the current position of the ETC inspection vehicle and the map through GPS positioning and map matching technology;

[0055] Prediction and analysis module, used to predict and analyze potential dangers and traffic flow based on collected environmental information;

[0056] The behavior decision and path planning module is used to make behavior decisions and path planning for ETC inspection vehicles based on task instructions and environmental analysis results;

[0057] A smoothing optimization processing module is used to smooth and optimize the generated path by setting smoothing strategies and optimization strategies;

[0058] A motion control instruction generation module, used for converting the formulated behavior decision into a motion control instruction;

[0059] A motion control instruction execution module is used for the ETC inspection vehicle to drive according to the motion control instruction and perform a predetermined inspection task;

[0060] The real-time monitoring and feedback adjustment module is used to continuously monitor environmental changes with on-board sensors during task execution, and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and path planning based on the uploaded data and task execution status;

[0061] The task completion evaluation module is used to evaluate the efficiency and effect of task execution after the task is completed;

[0062] The data recording and uploading module is used to record all data during the task execution and upload it to the designated parking cloud platform for subsequent analysis and optimization.

[0063] As a preferred solution of an intelligent inspection and payment device based on ETC and RFID, in the vehicle information identification unit, the environment perception and data acquisition processing module includes:

[0064] The environmental information collection module is used to collect the environmental information around the vehicle in real time through the laser radar, camera, millimeter wave radar and ultrasonic sensor carried by the ETC inspection vehicle to obtain the original collected data;

[0065] A preprocessing module is used to perform cropping, resizing and denoising on the collected raw data to obtain preprocessed sensor data;

[0066] The data fusion module is used to fuse the pre-processed sensor data through the sensor fusion algorithm to obtain fused data;

[0067] A three-dimensional environment model construction module, used to construct a three-dimensional environment model according to the fused data;

[0068] The vehicle information and parking space occupancy information acquisition module is used to process the fused data through a computer vision algorithm to acquire vehicle information; monitor the occupancy status and time of the parking space through geomagnetic equipment to acquire vehicle parking space occupancy information;

[0069] The information uploading module is used to upload the vehicle information and the vehicle parking space occupancy information to the designated parking cloud platform in real time.

[0070] As a preferred solution of an intelligent inspection and payment device based on ETC and RFID, in the berth matching confirmation unit, the berth matching confirmation module includes:

[0071] An RFID tag installation module for installing a unique RFID tag on each berth;

[0072] RFID tag information reading module, used to read the RFID tag information on the berth in real time through the RFID reader on the ETC inspection vehicle;

[0073] The berth matching and confirmation module is used to match and confirm the vehicle information of the identified vehicle with the berth information read by the RFID.

[0074] The present invention has the following advantages: by screening qualified unpaid orders through a designated parking cloud platform, arrears orders are obtained; the designated parking cloud platform pushes the arrears orders to the ETC middleware cloud platform in real time; after receiving the arrears orders, the ETC middleware cloud platform immediately synchronizes them to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received arrears orders; the ETC inspection vehicle inspects along the preset route through an unmanned driving control system; the ETC inspection vehicle collects and processes road images in real time through an AI recognition system to obtain vehicle information for identifying vehicles; the ETC inspection vehicle matches parking spaces through RFID technology to confirm that the identified vehicle is parked The location of the parking space; the ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle; the ETC inspection vehicle performs ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the arrears order; if the identified vehicle is not equipped with ETC, the road test recovery process is started; the ETC middleware cloud platform sends and saves the deduction success information in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle. The present invention can automatically identify road test vehicles, support ETC road test recovery, realize efficient and accurate automatic charging, improve road traffic management and charging efficiency; and reduce labor costs and error rates. The present invention effectively curbs evasion by supporting ETC road test recovery and automatic charging, and improves the fairness and transparency of highway toll management. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for the implementation methods or the prior art descriptions are briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0076] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0077] Figure 1A flow chart of an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0078] Figure 2 A schematic diagram of the working process of an ETC inspection vehicle in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0079] Figure 3 A schematic diagram of the ETC inspection vehicle system architecture in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0080] Figure 4 A schematic diagram of the ETC communication and supplementary payment processing flow in an ETC and RFID-based intelligent inspection and payment method provided in Example 1 of the present invention;

[0081] Figure 5 A schematic diagram of the environmental perception and data collection process in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0082] Figure 6 A schematic diagram of a specific processing flow of environmental perception and data collection in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0083] Figure 7 A schematic diagram of the decision-making and path planning process of an ETC inspection vehicle in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0084] Figure 8 A schematic diagram of a specific processing flow of decision making and path planning in an intelligent inspection and payment method based on ETC and RFID provided in Example 1 of the present invention;

[0085] Fig. 9 A schematic diagram of a berth matching confirmation process in an ETC and RFID-based intelligent inspection and payment method provided in Example 1 of the present invention;

[0086] Fig.10 A schematic diagram of the architecture of an intelligent inspection and payment device based on ETC and RFID provided in Example 2 of the present invention;

[0087] Fig.11 A schematic diagram of the module architecture of an ETC inspection vehicle inspection unit in an ETC and RFID-based intelligent inspection and payment device provided in Example 2 of the present invention;

[0088] Fig.12A schematic diagram of the module architecture of a vehicle information identification unit in an ETC and RFID-based intelligent inspection and payment device provided in Example 2 of the present invention;

[0089] Fig.13 A schematic diagram of the module architecture of a berth matching confirmation unit in an ETC and RFID-based intelligent inspection and payment device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0090] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] Example 1

[0092] See also Figure 1 , Figure 2 and Figure 3 Embodiment 1 of the present invention provides an intelligent inspection and payment method based on ETC and RFID, comprising:

[0093] S1. Screening qualified unpaid orders through a designated parking cloud platform to obtain arrears orders; pushing the arrears orders to the ETC middleware cloud platform in real time through the designated parking cloud platform;

[0094] S2. After receiving the overdue payment order, the ETC middleware cloud platform immediately synchronizes it to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received overdue payment order;

[0095] S3, ETC inspection vehicles use unmanned driving control systems to conduct inspections along preset routes;

[0096] S4, ETC inspection vehicles collect and process road images in real time through the AI ​​recognition system to obtain vehicle information for identifying vehicles;

[0097] S5. The ETC inspection vehicle performs parking space matching through RFID technology to confirm the location of the parking space where the identified vehicle is parked;

[0098] S6. The ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle;

[0099] S7, the ETC inspection vehicle performs ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; if the identified vehicle is not equipped with ETC, the road test recovery process is initiated;

[0100] S8. The ETC middleware cloud platform sends the deduction success information and saves it in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle.

[0101] In this embodiment, in step S1, qualified unpaid orders are screened through a designated parking cloud platform to obtain overdue orders; the overdue orders are pushed to the ETC middleware cloud platform in real time through the designated parking cloud platform; wherein, the designated parking cloud platform adopts the Lianlu Smart Parking Cloud Platform.

[0102] Specifically, Figure 4 As shown, the designated parking cloud platform first screens out eligible overdue orders and pushes these orders to the ETC middleware cloud platform in real time.

[0103] In this embodiment, in step S2, after receiving the overdue payment order, the ETC middleware cloud platform immediately synchronizes it to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received overdue payment order;

[0104] Specifically, after receiving the overdue payment order, the ETC middleware cloud platform will immediately synchronize it to the edge server of the ETC inspection vehicle (i.e., the inspection vehicle itself) to ensure the real-time nature of the overdue payment order data; the edge server of the ETC inspection vehicle will also store the received overdue payment order.

[0105] In this embodiment, in step S3, the ETC inspection vehicle performs inspection along a preset route through an unmanned driving control system;

[0106] Specifically, Figure 7 and Figure 8 As shown in the figure, the decision-making and path planning steps of the ETC inspection vehicle are:

[0107] S31, the ETC inspection vehicle receives the task instruction issued by the designated parking cloud platform;

[0108] Specifically, the ETC inspection vehicle first receives the task instructions from the designated parking cloud platform, including key information such as inspection area, inspection time, and task priority.

[0109] S32, collecting environmental information around the vehicle in real time through vehicle-mounted sensors;

[0110] Specifically, through a variety of on-board sensors (such as lidar, cameras, millimeter-wave radar, etc.), environmental information around the vehicle is collected in real time, including road conditions, obstacle locations, traffic flow, etc.

[0111] S33, determining the corresponding relationship between the current position of the ETC inspection vehicle and the map through GPS positioning and map matching technology;

[0112] Specifically, GPS positioning and map matching technology are used to determine the correspondence between the vehicle's current position and the map, providing basic data for subsequent decision-making and route planning.

[0113] S34. Predict and analyze potential dangers and traffic flow based on the collected environmental information;

[0114] S35. Make behavioral decisions and route planning for ETC inspection vehicles based on task instructions and environmental analysis results;

[0115] Specifically, according to the task instructions and environmental analysis results, the vehicle behavior decisions (such as acceleration, deceleration, steering, obstacle avoidance, etc.) and path planning are made. Path planning will comprehensively consider multiple factors such as road conditions, traffic rules, parking space distribution and task priority to generate the optimal driving path.

[0116] S36, smoothing and optimizing the generated path by setting a smoothing strategy and an optimization strategy;

[0117] Specifically, the generated path is smoothed and optimized to ensure the continuity and feasibility of the path and reduce the occurrence of sharp turns and frequent lane changes.

[0118] Among them, a B-spline curve or a Bezier curve is used for smoothing; the expression of the B-spline curve is:

[0119]

[0120] Where P(t) is a point on the curve; B i (t) is the basis function; P i It is the control point.

[0121] Use optimization algorithms such as gradient descent or simulated annealing to optimize the path; the expression of the gradient descent optimization algorithm is:

[0122]

[0123] Where θ is a parameter; η is the learning rate; J(θ) is the cost function.

[0124] S37, converting the formulated behavior decision into a motion control instruction;

[0125] Specifically, the decision results are converted into specific motion control instructions, such as throttle opening, braking force, steering angle, etc., for use by the vehicle actuators.

[0126] Among them, the PID control algorithm is used to generate control instructions such as throttle opening and braking force. The PID control expression is:

[0127]

[0128] Where u(t) is the control input; e(t) is the error; K p , K i , K d They are proportional, integral, and differential coefficients respectively.

[0129] S38, the ETC inspection vehicle drives according to the motion control instruction and performs the predetermined inspection task;

[0130] S39. During the execution of the task, the on-board sensors continuously monitor environmental changes and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and route planning based on the uploaded data and task execution status;

[0131] S310, after the task is completed, the efficiency and effect of the task execution are evaluated;

[0132] Specifically, after the task is completed, the efficiency and effectiveness of the task execution are evaluated, including indicators such as inspection coverage, path rationality, and time consumption.

[0133] S311. Record all data during the task execution and upload them to the designated parking cloud platform for subsequent analysis and optimization.

[0134] In this embodiment, in step S4, the ETC inspection vehicle collects and processes road images in real time through the AI ​​recognition system to obtain vehicle information of the identified vehicle;

[0135] Specifically, Figure 5 and Figure 6 As shown, the steps of environmental perception and data collection and processing are:

[0136] S41. Collect environmental information around the vehicle in real time through the laser radar, camera, millimeter-wave radar and ultrasonic sensor carried by the ETC inspection vehicle to obtain original collected data;

[0137] Specifically, LiDAR, cameras, millimeter-wave radar and ultrasonic sensors begin to collect real-time environmental information around the vehicle. LiDAR provides high-precision 3D point cloud data, cameras capture high-definition images, millimeter-wave radar measures distance and speed, and ultrasonic sensors are used to detect obstacles at close range.

[0138] S42, performing cropping, resizing and denoising preprocessing on the collected original data to obtain preprocessed sensor data;

[0139] Specifically, the collected raw data will go through preprocessing steps, including cropping, resizing (adjusting the image size), denoising and other operations to improve the efficiency and accuracy of subsequent processing.

[0140] Among them, the cropping process is to crop out irrelevant or redundant data areas according to actual application requirements and sensor characteristics. For example, for lidar data, a region of interest (ROI) can be set and only the point cloud data in the region is retained.

[0141] Resizing is the process of adjusting the image size to fit the input requirements of the subsequent processing algorithm. This usually involves image scaling operations, which can use methods such as bilinear interpolation or nearest neighbor interpolation. The mathematical expression for resizing is:

[0142] I resized (x,y)=I original (scale x x, scale y y)

[0143] In the formula, I original is the original image; I resized is the resized image; scale x and scale y are the scaling factors for the horizontal and vertical directions respectively.

[0144] Denoising is the process of applying filtering algorithms to remove noise interference in data. For image data, Gaussian filtering, mean filtering, etc. can be used. For lidar data, statistical filtering or voxel filtering can be used to remove noise and outliers.

[0145] S43, fusing the preprocessed sensor data through a sensor fusion algorithm to obtain fused data;

[0146] Specifically, advanced sensor fusion algorithms (such as Kalman filtering, particle filtering, etc.) are used to fuse data from different sensors to obtain fused data.

[0147] Among them, the Kalman filter fusion algorithm is used for state estimation and smooth motion trajectory of continuous data. Kalman filter minimizes the estimation error by recursively updating the state estimation and covariance matrix.

[0148] The particle filter fusion algorithm is used in complex or nonlinear scenarios. The particle filter simulates the system state through a large number of particles and updates the particle weights according to the observed values ​​to achieve more robust data fusion. Its key steps include initializing particles, predicting particle states, updating particle weights, resampling, and estimating system states.

[0149] Fusion processing combines time synchronization, spatial alignment and other methods to fuse the data of different sensors in a unified coordinate system. For example, the data of different sensors can be aligned using timestamps, and the data of different sensors can be converted to the same reference coordinate system using coordinate transformations (such as rotation matrices and translation vectors).

[0150] S44, constructing a three-dimensional environment model according to the fused data;

[0151] Specifically, based on the fused sensor data, a three-dimensional environment model or a bird's-eye view (BEV map) around the vehicle is constructed to enable a more intuitive understanding of the scene.

[0152] The 3D environment model uses a point cloud processing library (such as PCL) to process the fused lidar data to generate a 3D point cloud model, which usually involves operations such as point cloud filtering, segmentation, registration, and surface reconstruction.

[0153] S45, processing the fused data by a computer vision algorithm to obtain vehicle information; monitoring the occupancy status and time of the parking space by a geomagnetic device to obtain vehicle parking space occupancy information;

[0154] Specifically, computer vision algorithms are used to process image data to achieve license plate recognition, vehicle detection, etc. At the same time, geomagnetic equipment is used to monitor the occupancy status and time of berths.

[0155] Wherein, in the process of processing the fused data by a computer vision algorithm to obtain the vehicle information, the steps of identification processing are:

[0156] S451, performing denoising and enhancement preprocessing on the captured vehicle image;

[0157] Specifically, the captured vehicle images are preprocessed by denoising, enhancing, etc. to improve the image quality.

[0158] S452, extracting vehicle features of the identified vehicle through a deep learning network;

[0159] Specifically, a deep learning network (such as a convolutional neural network (CNN)) is used to extract vehicle features, including license plate number, vehicle model, color, etc.

[0160] S453: Based on the extracted vehicle features, perform vehicle identification through a classifier and output the vehicle information.

[0161] Specifically, based on the extracted features, a classifier (such as a support vector machine (SVM), a neural network, etc.) is used to perform vehicle recognition and output vehicle information.

[0162] S46. Upload the vehicle information and the vehicle parking space occupancy information to a designated parking cloud platform in real time.

[0163] Specifically, all key data collected (including license plate number, vehicle type, parking space status, parking time, etc.) will be uploaded to the designated parking cloud platform in real time. The designated parking cloud platform will further process this data for management functions such as parking fee settlement and parking space monitoring.

[0164] In this embodiment, in step S5, the ETC inspection vehicle performs parking space matching through RFID technology to confirm the location of the parking space where the identified vehicle is parked;

[0165] Specifically, Fig. 9 As shown, the steps for berth matching confirmation are:

[0166] S51. Install a unique RFID tag on each berth;

[0167] Specifically, a unique RFID tag is installed on each berth, and the tag contains the berth number, location information, etc.

[0168] S52, read the RFID tag information on the berth in real time through the RFID reader / writer on the ETC inspection vehicle;

[0169] S53: Match and confirm the vehicle information of the identified vehicle with the berth information read by the RFID.

[0170] Specifically, the vehicle identification result is matched with the parking space information read by RFID. When the inspection vehicle passes by the parking space, the system simultaneously obtains the vehicle image and RFID tag information. By comparing the vehicle position with the RFID tag position, combined with information such as timestamp, the vehicle and parking space are accurately matched.

[0171] In this embodiment, in step S6, the ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle.

[0172] In this embodiment, in step S7, the ETC inspection vehicle performs ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; if the identified vehicle is not equipped with ETC, the road test recovery process is started;

[0173] Specifically, Figure 4 As shown, the ETC inspection vehicle edge server stores these overdue payment orders. During the inspection process, the inspection vehicle will read the OBU information loaded on the vehicle through the vehicle-mounted ETC antenna, and query whether the vehicle has overdue payment information on the edge server of the ETC inspection vehicle through the ETC antenna module. If there is overdue payment information, it will be transferred to the third-party deduction channel through the ETC middleware cloud platform, and the bank card associated with etc will be deducted. If the identified vehicle is not equipped with ETC, the reading fails or the tag is invalid, the road test recovery process is started.

[0174] In this embodiment, in step S8, the ETC middleware cloud platform sends down and saves the deduction success information in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle.

[0175] Specifically, once the user completes the supplementary payment, the ETC inspection vehicle edge server will push the successful deduction information back to the ETC middleware cloud platform to ensure timely feedback of the information. The successful deduction information is then written to the on-board OBU module through the downlink channel, and the successful deduction information is sent to the ETC app that identifies the owner of the vehicle. For vehicles in abnormal conditions (such as blacklisted vehicles), the bank's deduction center or settlement center will handle the deduction failure, including deduction query, black and white gray list verification, and ensure that the ETC channels in various provinces in the country can be opened to ensure the smooth progress of the deduction process.

[0176] In summary, the present invention obtains overdue payment orders by screening qualified unpaid orders through a designated parking cloud platform; pushes the overdue payment orders to the ETC middleware cloud platform in real time through the designated parking cloud platform; after receiving the overdue payment orders, the ETC middleware cloud platform immediately synchronizes them to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received overdue payment orders; the ETC inspection vehicle inspects along the preset route through an unmanned driving control system; the ETC inspection vehicle collects and processes road images in real time through an AI recognition system to obtain vehicle information for identifying vehicles; the ETC inspection vehicle matches parking spaces through RFID technology to confirm that the identified vehicle is parked The location of the parking space; the ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle; the ETC inspection vehicle performs ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the arrears order; if the identified vehicle is not equipped with ETC, the road test recovery process is started; the ETC middleware cloud platform sends and saves the deduction success information in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle. The present invention can automatically identify road test vehicles, support ETC road test recovery, realize efficient and accurate automatic charging, improve road traffic management and charging efficiency; and reduce labor costs and error rates. The present invention effectively curbs evasion by supporting ETC road test recovery and automatic charging, and improves the fairness and transparency of highway toll management.

[0177] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0178] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0179] Example 2

[0180] See also Fig.10 Embodiment 2 of the present invention provides an intelligent inspection and payment device based on ETC and RFID, comprising:

[0181] The overdue order acquisition and push unit 001 is used to screen qualified unpaid orders through a designated parking cloud platform to obtain overdue orders; and push the overdue orders to the ETC middleware cloud platform in real time through the designated parking cloud platform;

[0182] The overdue payment order receiving and storage unit 002 is used for the ETC middleware cloud platform to immediately synchronize the overdue payment order to the edge server of the ETC inspection vehicle after receiving the overdue payment order; the edge server of the ETC inspection vehicle stores the received overdue payment order;

[0183] ETC inspection vehicle inspection unit 003, used for ETC inspection vehicles to inspect along preset routes through unmanned driving control systems;

[0184] Vehicle information recognition unit 004, used for the ETC inspection vehicle to collect and process road images in real time through the AI ​​recognition system to obtain vehicle information for identifying the vehicle;

[0185] The parking space matching confirmation unit 005 is used for the ETC inspection vehicle to match the parking space through RFID technology to confirm the position of the parking space of the identified vehicle;

[0186] The charging information acquisition unit 006 is used for the ETC inspection vehicle to upload the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the charging information of the identified vehicle, and sends the charging information to the edge server of the ETC inspection vehicle;

[0187] The deduction processing unit 007 is used for the ETC inspection vehicle to perform ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; if the identified vehicle is not equipped with ETC, the road test recovery process is started;

[0188] The deduction information sending unit 008 is used for the ETC middleware cloud platform to send the deduction success information and save it in the obu of the on-board ETC of the identified vehicle, and send the deduction success information to the ETC app of the owner of the identified vehicle.

[0189] In this embodiment, Fig.11 As shown, in the ETC inspection vehicle inspection unit 003, the decision-making and path planning module of the ETC inspection vehicle includes:

[0190] Task instruction receiving module 031, used for the ETC inspection vehicle to receive task instructions issued by the designated parking cloud platform;

[0191] The environmental information collection module 032 is used to collect the environmental information around the vehicle in real time through the vehicle-mounted sensors;

[0192] The vehicle location positioning module 033 is used to determine the corresponding relationship between the current position of the ETC inspection vehicle and the map through GPS positioning and map matching technology;

[0193] Prediction and analysis module 034, used to predict and analyze potential dangers and traffic flow based on the collected environmental information;

[0194] The behavior decision and path planning module 035 is used to make behavior decisions and path planning for the ETC inspection vehicle according to the task instructions and environmental analysis results;

[0195] The smoothing optimization processing module 036 is used to smooth and optimize the generated path by setting a smoothing strategy and an optimization strategy;

[0196] A motion control instruction generating module 037, used for converting the formulated behavior decision into a motion control instruction;

[0197] The motion control instruction execution module 038 is used for the ETC inspection vehicle to drive according to the motion control instruction and perform the predetermined inspection task;

[0198] The real-time monitoring and feedback adjustment module 039 is used for the on-board sensors to continuously monitor environmental changes during the task execution process, and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and path planning according to the uploaded data and task execution status;

[0199] The task completion evaluation module 0310 is used to evaluate the efficiency and effect of task execution after the task is completed;

[0200] The data recording and uploading module 0311 is used to record all data during the task execution and upload it to the designated parking cloud platform for subsequent analysis and optimization.

[0201] In this embodiment, Fig.12 As shown, in the vehicle information recognition unit 004, the environment perception and data acquisition processing module includes:

[0202] Environmental information collection module 041 is used to collect environmental information around the vehicle in real time through the laser radar, camera, millimeter wave radar and ultrasonic sensor carried by the ETC inspection vehicle to obtain original collected data;

[0203] The preprocessing module 042 is used to perform cropping, resizing and denoising on the collected raw data to obtain preprocessed sensor data;

[0204] The data fusion module 043 is used to fuse the pre-processed sensor data through a sensor fusion algorithm to obtain fused data;

[0205] A three-dimensional environment model building module 044 is used to build a three-dimensional environment model according to the fused data;

[0206] The vehicle information and parking space occupancy information acquisition module 045 is used to process the fused data through a computer vision algorithm to acquire vehicle information; monitor the occupancy status and time of the parking space through geomagnetic equipment to acquire vehicle parking space occupancy information;

[0207] The information uploading module 046 is used to upload the vehicle information and the vehicle parking space occupancy information to the designated parking cloud platform in real time.

[0208] In this embodiment, Fig.12 As shown, in the vehicle information identification unit 004, in the vehicle information and berth occupancy information acquisition module 045, the vehicle information identification processing submodule includes:

[0209] The image preprocessing submodule 0451 is used to perform denoising and enhancement preprocessing on the captured vehicle image;

[0210] A feature extraction submodule 0452 is used to extract the vehicle features of the identified vehicle through a deep learning network;

[0211] The vehicle identification submodule 0453 is used to identify the vehicle through a classifier according to the extracted vehicle features and output the vehicle information.

[0212] In this embodiment, Fig.13 As shown, in the berth matching confirmation unit 005, the berth matching confirmation module includes:

[0213] RFID tag installation module 051, used to install a unique RFID tag on each berth;

[0214] RFID tag information reading module 052, used to read the RFID tag information on the berth in real time through the RFID reader on the ETC inspection vehicle;

[0215] The berth matching and confirmation module 053 is used to match and confirm the vehicle information of the identified vehicle with the berth information read by the RFID.

[0216] It should be noted that the information interaction, execution process, etc. between the modules / units of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0217] Example 3

[0218] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for an intelligent patrol and payment method based on ETC and RFID is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of an intelligent patrol and payment method based on ETC and RFID.

[0219] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0220] Example 4

[0221] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0222] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute an intelligent patrol and payment method based on ETC and RFID in Example 1 or any possible implementation thereof.

[0223] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0224] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0225] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0226] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. An intelligent inspection and payment method based on ETC and RFID, characterized in that: include: Screen the qualified unpaid orders through the designated parking cloud platform to obtain the overdue orders; Pushing the overdue payment order to the ETC middleware cloud platform in real time through the designated parking cloud platform; After receiving the overdue payment order, the ETC middleware cloud platform immediately synchronizes it to the edge server of the ETC inspection vehicle; the edge server of the ETC inspection vehicle stores the received overdue payment order; The ETC inspection vehicle uses an unmanned driving control system to conduct inspections along a preset route; The ETC inspection vehicle collects and processes road images in real time through the AI ​​recognition system to obtain vehicle information for identifying vehicles; The ETC inspection vehicle uses RFID technology to match parking spaces and confirm the location of the parking space where the identified vehicle is parked; The ETC inspection vehicle uploads the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the billing information of the identified vehicle, and sends the billing information to the edge server of the ETC inspection vehicle; The ETC inspection vehicle performs ETC deduction processing on the identified vehicle with ETC according to the billing information and the overdue payment order; If the identified vehicle is not equipped with ETC, the road test recovery process is initiated; The ETC middleware cloud platform sends down the deduction success information and saves it in the obu of the on-board ETC of the identified vehicle, and sends the deduction success information to the ETC app of the owner of the identified vehicle.

2. According to claim 1, the intelligent inspection and payment method based on ETC and RFID is characterized in that: When the ETC inspection vehicle is inspected along the preset route through the unmanned driving control system, the decision-making and path planning steps of the ETC inspection vehicle are as follows: The ETC inspection vehicle receives the task instructions from the designated parking cloud platform; Collect environmental information around the vehicle in real time through on-board sensors; Through GPS positioning and map matching technology, determine the corresponding relationship between the current position of the ETC inspection vehicle and the map; Predict and analyze potential dangers and traffic flow based on collected environmental information; Make behavioral decisions and route planning for ETC inspection vehicles based on mission instructions and environmental analysis results; By setting smoothing strategy and optimization strategy, the generated path is smoothed and optimized; converting the formulated behavioral decisions into movement control instructions; The ETC inspection vehicle drives according to the motion control instructions and performs the predetermined inspection tasks; During the execution of the task, the on-board sensors continuously monitor environmental changes and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and route planning based on the uploaded data and task execution status; After the task is completed, the efficiency and effectiveness of the task execution are evaluated; All data during the task execution will be recorded and uploaded to the designated parking cloud platform for subsequent analysis and optimization.

3. According to claim 2, the intelligent inspection and payment method based on ETC and RFID is characterized in that: When the ETC inspection vehicle collects road images in real time through the AI ​​recognition system, the steps of environmental perception and data collection and processing are as follows: The ETC inspection vehicle uses laser radar, cameras, millimeter-wave radar, and ultrasonic sensors to collect environmental information around the vehicle in real time and obtain original data. The collected raw data is cropped, resized and denoised to obtain preprocessed sensor data; Through the sensor fusion algorithm, the pre-processed sensor data is fused to obtain fused data; constructing a three-dimensional environment model according to the fused data; Processing the fused data through a computer vision algorithm to obtain vehicle information; Through geomagnetic equipment, the occupancy status and time of parking spaces are monitored to obtain parking space occupancy information; The vehicle information and the vehicle parking space occupancy information are uploaded to the designated parking cloud platform in real time.

4. According to claim 3, the intelligent inspection and payment method based on ETC and RFID is characterized in that: In the process of processing the fused data by a computer vision algorithm to obtain the vehicle information, the steps of identification processing are: Perform denoising and enhancement preprocessing on the captured vehicle images; Extracting vehicle features of the identified vehicle through a deep learning network; According to the extracted vehicle features, the vehicle is identified by a classifier and the vehicle information is output.

5. According to claim 4, the intelligent inspection and payment method based on ETC and RFID is characterized in that: When the ETC inspection vehicle uses RFID technology to match parking spaces and confirm the position of the identified vehicle parking space, the parking space matching confirmation steps are as follows: Installing unique RFID tags on each berth; The RFID tag information on the berth is read in real time through the RFID reader on the ETC inspection vehicle; The vehicle information of the identified vehicle is matched and confirmed with the berth information read by the RFID.

6. An intelligent inspection and payment device based on ETC and RFID, adopting an intelligent inspection and payment method based on ETC and RFID according to any one of claims 1 to 5, characterized in that: include: The overdue order acquisition and push unit is used to screen the unpaid orders that meet the conditions through the designated parking cloud platform and obtain the overdue orders; Pushing the overdue payment order to the ETC middleware cloud platform in real time through the designated parking cloud platform; The overdue payment order receiving and storage unit is used for the ETC middleware cloud platform to immediately synchronize the overdue payment order to the edge server of the ETC inspection vehicle after receiving the overdue payment order; the edge server of the ETC inspection vehicle stores the received overdue payment order; The ETC inspection vehicle inspection unit is used for the ETC inspection vehicle to inspect along the preset route through the unmanned driving control system; The vehicle information recognition unit is used by the ETC inspection vehicle to collect and process road images in real time through the AI ​​recognition system to obtain vehicle information for identifying the vehicle; The parking space matching confirmation unit is used by the ETC inspection vehicle to match the parking space through RFID technology to confirm the position of the identified vehicle parking space; The charging information acquisition unit is used for the ETC inspection vehicle to upload the vehicle information and the road image to the designated parking cloud platform; the designated parking cloud platform processes the vehicle information and the road image through a set process, obtains the charging information of the identified vehicle, and sends the charging information to the edge server of the ETC inspection vehicle; A deduction processing unit, used for the ETC inspection vehicle to perform ETC deduction processing on the identified vehicle equipped with ETC according to the billing information and the overdue payment order; If the identified vehicle is not equipped with ETC, the road test recovery process is initiated; The deduction information sending unit is used for the ETC middleware cloud platform to send the deduction success information and save it in the obu of the on-board ETC of the identified vehicle, and send the deduction success information to the ETC app of the owner of the identified vehicle.

7. According to claim 6, the intelligent inspection and payment device based on ETC and RFID is characterized in that: In the ETC inspection vehicle inspection unit, the decision-making and path planning module of the ETC inspection vehicle includes: Task instruction receiving module, used for ETC inspection vehicles to receive task instructions issued by the designated parking cloud platform; Environmental information collection module, used to collect environmental information around the vehicle in real time through on-board sensors; The vehicle location positioning module is used to determine the corresponding relationship between the current position of the ETC inspection vehicle and the map through GPS positioning and map matching technology; Prediction and analysis module, used to predict and analyze potential dangers and traffic flow based on collected environmental information; The behavior decision and path planning module is used to make behavior decisions and path planning for ETC inspection vehicles based on task instructions and environmental analysis results; A smoothing optimization processing module is used to smooth and optimize the generated path by setting smoothing strategies and optimization strategies; A motion control instruction generation module, used for converting the formulated behavior decision into a motion control instruction; A motion control instruction execution module is used for the ETC inspection vehicle to drive according to the motion control instruction and perform a predetermined inspection task; The real-time monitoring and feedback adjustment module is used to continuously monitor environmental changes with on-board sensors during task execution, and upload the monitoring results to the designated parking cloud platform in real time; the designated parking cloud platform sends new instructions to the ETC inspection vehicle or adjusts the original decision and path planning based on the uploaded data and task execution status; The task completion evaluation module is used to evaluate the efficiency and effect of task execution after the task is completed; The data recording and uploading module is used to record all data during the task execution and upload it to the designated parking cloud platform for subsequent analysis and optimization.

8. According to claim 7, the intelligent inspection and payment device based on ETC and RFID is characterized in that: In the vehicle information recognition unit, the environment perception and data acquisition processing module includes: The environmental information collection module is used to collect the environmental information around the vehicle in real time through the laser radar, camera, millimeter wave radar and ultrasonic sensor carried by the ETC inspection vehicle to obtain the original collected data; A preprocessing module is used to perform cropping, resizing and denoising on the collected raw data to obtain preprocessed sensor data; The data fusion module is used to fuse the pre-processed sensor data through the sensor fusion algorithm to obtain fused data; A three-dimensional environment model construction module, used to construct a three-dimensional environment model according to the fused data; The vehicle information and parking space occupancy information acquisition module is used to process the fused data through a computer vision algorithm to acquire vehicle information; monitor the occupancy status and time of the parking space through geomagnetic equipment to acquire vehicle parking space occupancy information; The information uploading module is used to upload the vehicle information and the vehicle parking space occupancy information to the designated parking cloud platform in real time.

9. The intelligent inspection and payment device based on ETC and RFID according to claim 8 is characterized in that: In the vehicle information identification unit, in the vehicle information and berth occupancy information acquisition module, the vehicle information identification processing submodule includes: The image preprocessing submodule is used to perform denoising and enhancement preprocessing on the captured vehicle images; A feature extraction submodule, used for extracting vehicle features of the identified vehicle through a deep learning network; The vehicle identification submodule is used to perform vehicle identification through a classifier according to the extracted vehicle features and output the vehicle information.

10. The intelligent inspection and payment device based on ETC and RFID according to claim 9 is characterized in that: In the berth matching confirmation unit, the berth matching confirmation module includes: An RFID tag installation module for installing a unique RFID tag on each berth; RFID tag information reading module, used to read the RFID tag information on the berth in real time through the RFID reader on the ETC inspection vehicle; The berth matching and confirmation module is used to match and confirm the vehicle information of the identified vehicle with the berth information read by the RFID.

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