An evaluation system and method for detecting road operating conditions using a vehicle-mounted laser radar

Abstract

The present application relates to the field of road condition evaluation maintenance technology, and is an evaluation system and method for detecting road operation conditions by using a vehicle-mounted laser radar; the system comprises a vehicle-mounted laser radar acquisition unit, a 3D cloud data unit, a road operation condition evaluation unit and a road condition feedback unit; the 3D cloud data unit receives road surface condition data and processes the data to complete road condition parameter identification, wherein the data processing comprises data denoising and missing value filling; the evaluation system for detecting road conditions by using a vehicle-mounted laser radar combines new energy intelligent driving vehicles with road condition detection equipment, can realize dynamic collection of road condition technical parameters, and converts the parameters into prompt slogans and voice prompts, thereby effectively solving the problems of high cost and high price of special road condition detection equipment; the vehicle-mounted laser radar collects road conditions and feeds back the road conditions to road management departments and vehicle receiving ends in real time, improves the road condition detection efficiency of the road management departments, and ensures the driving safety of the drivers of the vehicle receiving ends.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of road condition evaluation maintenance technology, and in particular to a system and method for evaluating road operation conditions using a vehicle-mounted laser radar. BACKGROUND

[0002] The vehicle-mounted laser radar is one of the core sensors in automatic driving and advanced driving assistance systems (ADAS), which detects the distance, shape and motion state of the surrounding environment by emitting laser beams and receiving reflected signals. The vehicle-mounted laser radar uses laser beam emission and reflection to form dense 3D point cloud data, and then constructs a high-precision environment model.

[0003] Currently, road congestion, road collapse, road ponding (waterlogging), manhole cover damage and other road operation conditions are the focus of public concern, and how to ensure the safe and stable operation of the road is one of the core responsibilities of the municipal management department. Road operation conditions change dynamically at all times, and to ensure their safe and stable operation, the first step is to detect their operation conditions.

[0004] Currently, there are various means for detecting road operation conditions, including traditional manual inspection, buried sensors (coils), and modern detection technologies such as high-definition cameras + computer vision technology, radar technology, infrared sensors, mobile detection vehicles, and satellite remote sensing technology. SUMMARY

[0005] The present application provides a system and method for evaluating road operation conditions using a vehicle-mounted laser radar, which overcomes the shortcomings of the prior art and effectively solves the problem of high cost of using special equipment for road condition detection.

[0006] To solve the above problems, one of the technical solutions of the present application is achieved by the following way: a system for evaluating road operation conditions using a vehicle-mounted laser radar, comprising a vehicle-mounted laser radar acquisition unit, a 3D cloud data unit, a road operation condition evaluation unit and a road condition feedback unit;

[0007] The vehicle-mounted laser radar acquisition unit acquires real-time road condition data;

[0008] The 3D cloud data unit receives the road condition data and processes the data to complete road condition parameter identification, wherein the data processing includes data denoising, missing value filling, keyword extraction and data fusion;

[0009] The road operation condition evaluation unit constructs a road condition environment model according to the road condition parameters, and evaluates the damage degree of the road condition disaster according to the road condition environment, and outputs the road condition evaluation result; wherein the damage degree of the disaster includes single damage, small area damage and large area damage;

[0010] The road condition feedback unit receives the road condition evaluation result and feeds back to the vehicle receiving end and the road management department, reminding the vehicle driver to avoid the disaster damaged road section, and whether the disaster damaged road section needs to be repaired is analyzed by the road management department.

[0011] The 3D cloud data unit includes a data stream processing module and a road condition parameter identification module.

[0012] The data stream processing module denoises, fills in missing values, extracts keywords and fuses data for the received road surface condition data, and outputs the processed data to the road condition parameter identification module.

[0013] The road condition parameter identification module completes road condition parameter identification according to the processed data, wherein the road condition parameters include road obstacles, road defects, road cracks and road subsidence.

[0014] The road operation condition evaluation unit includes a construction environment model module, a system evaluation module and a road condition evaluation result module.

[0015] The construction environment model module identifies road disasters under different road condition parameters, selects algorithms, verifies algorithm accuracy, completes road condition environment model construction, and evaluates different road condition types through the road condition environment module.

[0016] The system evaluation module evaluates the damage degree of road condition disasters according to the existing specifications according to different road condition types, and according to the preset evaluation label, inputs different road condition disaster prompt marks and voice prompts, and outputs road condition evaluation results; wherein the damage degree includes single damage, small area damage and large area damage.

[0017] The road condition evaluation result module receives the road condition evaluation result and outputs the road condition evaluation result to the road condition feedback unit; wherein the road condition evaluation result includes road abnormal subsidence, cavity; road obstacle; road surface more than 50mm; manhole cover, rainwater inlet grille loss or serious damage; serious water accumulation and icing on the road.

[0018] The system evaluation module includes a roadbed and pavement evaluation module, a bridge evaluation module, a tunnel evaluation module, a pipeline evaluation module and a traffic and auxiliary facility evaluation module.

[0019] The road condition feedback unit includes a road management feedback module and a user feedback module.

[0020] The road management feedback module communicates with the road management department, receives the road condition evaluation result output by the system evaluation module, and issues a perfect road condition evaluation report, identifies the accurate position and damage degree of different damage types, and gives the evaluation result to the road management department according to the road maintenance specification;

[0021] The user feedback module communicates with the vehicle receiving end, combines the road condition evaluation result output by the system evaluation module, is loaded into the vehicle-mounted navigation system of the vehicle receiving end, and feeds back the road condition in real time.

[0022] The second technical solution of the application is realized by the following method: a method for evaluating road operation conditions by using a vehicle-mounted laser radar, comprising:

[0023] Real-time collection of road surface condition data;

[0024] Receiving the road surface condition data and processing the data to complete road condition parameter identification, wherein the data processing includes data denoising, missing value filling, keyword extraction and data fusion;

[0025] According to the road condition parameters, a road condition environment model is constructed, and according to the road condition environment, the damage degree of the road condition disaster is evaluated, and a road condition evaluation result is output;

[0026] The road condition evaluation result is received and fed back to the vehicle receiving end and the road management department, reminding the vehicle driver to avoid the disaster damaged road section, and the road management department analyzes whether the disaster damaged road section needs to be repaired.

[0027] The above-mentioned construction of the road condition environment model according to the road condition parameters, and the evaluation of the damage degree of the road condition disaster according to the road condition environment, and the output of the road condition evaluation result, comprising:

[0028] According to the identification of the road disaster under different road condition parameters, the algorithm is selected, the accuracy of the algorithm is verified, the construction of the road condition environment model is completed, and different road condition types are evaluated through the road condition environment module;

[0029] According to different road condition types, the damage degree of the road condition disaster is evaluated according to the existing specification, and different road condition disaster prompt labels and voice prompts are input according to the preset evaluation labels, and the road condition evaluation result is output;

[0030] The road condition evaluation result is received and fed back to the road management department and the vehicle receiving end in time; wherein, the feedback road condition result includes that the road appears abnormal subsidence, cavity; the road appears obstacles; the road surface appears more than 50mm of wrong table; the manhole cover, the rainwater inlet grating is lost or seriously damaged; the road surface appears serious water accumulation, icing.

[0031] The evaluation system for detecting road conditions by the vehicle-mounted laser radar is provided, the new energy intelligent driving vehicle is combined with the road condition detection equipment, technical parameters of dynamically collecting road conditions can be realized, and the technical parameters are converted into prompt slogans and voice prompts, wherein, after a large area of damaged conditions is collected, the avoidance instructions can be output after being judged by the system evaluation module, and are synchronously transmitted to the vehicle receiving end or the driver of the vehicle receiving end, and are avoided by the intelligent driving vehicle algorithm or the driver in time; when small area damaged or single damaged is collected, the avoidance instructions can be used as system prompts to trigger the early deceleration instructions, thereby, the problems of high cost and high price of the special road condition detection equipment are effectively solved, the road conditions are collected by the vehicle-mounted laser radar, and the road conditions are fed back to the road management department and the vehicle receiving end in real time, the road condition detection efficiency of the road management department is improved, and the driving safety of the driver of the vehicle receiving end is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0032] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0033] Figure 1 The system block diagram of the embodiment 1 of the present application is shown.

[0034] Figure 2 The system block diagram of the 3D cloud data unit in the embodiment 1 of the present application is shown.

[0035] Figure 3 The method flow chart of the embodiment 2 of the present application is shown. DETAILED DESCRIPTION

[0036] The present application is not limited by the following embodiments, and the specific embodiments can be determined according to the technical solutions of the present application and the actual situation.

[0037] Embodiment 1: as shown in the following table, the embodiment of the present application discloses an evaluation system for detecting road operation conditions by using a vehicle-mounted laser radar, which comprises a vehicle-mounted laser radar acquisition unit, a 3D cloud data unit, a road operation condition evaluation unit and a road condition feedback unit; Figure 1

[0038] The vehicle-mounted laser radar acquisition unit acquires road surface condition data in real time;

[0039] The 3D cloud data unit receives the road surface condition data, processes the data, and completes road condition parameter identification, wherein the data processing includes data denoising, missing value filling, keyword extraction and data fusion;

[0040] The road operation condition evaluation unit constructs a road condition environment model according to the road condition parameters, evaluates the damage degree of the road condition disaster according to the road condition environment, and outputs the road condition evaluation result; wherein, the damage degree of the disaster includes single damage, small area damage and large area damage;

[0041] ​The road condition feedback unit receives the road condition evaluation result and feeds back to the vehicle receiving end and the road management department, reminding the vehicle driver to avoid the disaster damaged road section, and the road management department analyzes whether the disaster damaged road section needs to be repaired.

[0042] The vehicle-mounted laser radar collecting unit can be a known vehicle-mounted laser radar. Most of the vehicle-mounted laser radars are solid-state laser radars, which have no or few moving parts. A single forward laser radar or multiple laser radars are usually configured, which have the advantages of strong anti-interference, speed measurement, higher reliability and longer service life. The vehicle-mounted solid-state laser radar transmits the dense 3D cloud data to the 3D cloud data unit through the laser beam emission and data reception.

[0043] The road management department analyzes whether the disaster damaged road section needs to be repaired, specifically: after collecting large area damage, the road management department outputs prompt slogans and voice prompts to the relevant road section or the driver user of the vehicle receiving end, reminding the vehicle to avoid the disaster damaged road section, and timely making the instruction to repair the disaster damaged road section to the staff; when collecting small area damage or single damage, it can be used as a system prompt and output to the driver user of the vehicle receiving end to trigger the early deceleration instruction.

[0044] As shown in Figure 2 The 3D cloud data unit includes a data stream processing module and a road condition parameter identification module. The data stream processing module denoises, fills in missing values, extracts keywords and fuses data for the received road surface condition data, and outputs the processed data to the road condition parameter identification module. The road condition parameter identification module completes the road condition parameter identification according to the processed data, wherein the road condition parameters include road obstacles, road defects, road cracks, and road subsidence. The data fusion specifically refers to denoising, filling in missing values, and extracting road surface related key parameters for the road surface condition data, and performing data fusion on the time and space aligned multi-source data.

[0045] The time and space aligned multi-source data includes vehicle-mounted laser radar data, traffic management system, map service provider, satellite remote sensing data, etc.

[0046] The road operation condition evaluation unit includes a construction environment model module, a system evaluation module and a road condition evaluation result module.

[0047] The construction environment model module identifies road disasters under different road condition parameters, selects an algorithm, verifies the accuracy of the algorithm, completes the construction of the road condition environment model, and evaluates different road condition types through the road condition environment module. Thus, the construction environment model module optimizes the deep learning algorithm, determines that the deep learning algorithm is specifically a generative adversarial network (GAN), verifies the accuracy of the algorithm through cross-validation, makes the construction environment model constructed by the construction environment model module more perfect, accurately identifies the detection data of the vehicle-mounted laser radar, converts the data into electrical signals, identifies different electrical signals to represent different road condition parameters, and feeds back the evaluation results to the system evaluation module in a timely manner.

[0048] The system evaluation module evaluates the damage degree of road disasters according to different road condition types and current specifications, inputs different road disaster prompt labels and voice prompts according to preset evaluation labels, and outputs road condition evaluation results. For example, the evaluation of road flatness first calculates the international roughness index (IRI) of the road section according to the collected data, determines different limits according to the road grade and service life, and identifies the road section and feeds back the evaluation results when the international roughness index (IRI) exceeds the limit. For example, the evaluation of asphalt pavement damage first calculates the area and density of each type of damage according to the collected data, then calculates the pavement condition index (PCI) of the road section, determines different limits according to the road grade and service life, and identifies the road section and feeds back the evaluation results when the pavement condition index (PCI) exceeds the limit. Thus, the system evaluation module combines road maintenance specifications, presets road condition damage labels and labels, establishes a system evaluation label library, and evaluates the damage degree of road disasters. The prompt labels and voice prompts are set according to different types and degrees of damage, the voice prompts are given according to the degree of damage affecting driving safety, the text labels are prompted according to the degree of damage affecting driving comfort, and the color labels are prompted according to the degree of damage affecting driving path selection.

[0049] The road condition evaluation result module receives the road condition evaluation results and outputs the road condition evaluation results to the road condition feedback unit. The road condition evaluation results include abnormal subsidence, cavities, obstacles, road surface misalignment greater than 100 mm, missing or severely damaged manhole covers and rainwater grates, and severe water accumulation and icing on the road.

[0050] The system evaluation module includes a roadbed and pavement evaluation module, a bridge evaluation module, a tunnel evaluation module, a pipeline evaluation module, and a traffic and ancillary facility evaluation module. Thus, the system evaluation module is divided into different modules according to different road conditions, and independent data label libraries are established. The data identified by the construction environment model module is input into different modules at the same time, and the system evaluation module selects suitable data labels and removes duplicate and abnormal data.

[0051] The pavement conditions evaluated in the above roadbed and pavement evaluation module include flatness, damage (cracks, potholes, subsidence, etc.), obstacles (garbage or falling objects), water accumulation, icing (frozen ice), retaining protection (retaining walls, facing walls, crash barriers, etc.), construction impact, etc.

[0052] The pavement conditions evaluated in the bridge evaluation module include bridge deck pavement (same as pavement), expansion device (looseness, abnormal deformation, damage, shedding, obstruction), bridge head bump, railings, crash barriers (damage, missing, breakage, etc.), protective nets, sound barriers (damage, deformation, etc.), etc.

[0053] The pavement conditions evaluated in the tunnel evaluation module include tunnel pavement (same as pavement), lining (cracking, deformation, subsidence, corrosion and peeling), tunnel entrance (water accumulation and icing, falling rocks, subsidence, bumping), ventilation facilities (rotation), lighting facilities, etc.

[0054] The pavement conditions evaluated in the pipeline evaluation module include pipeline inspection wells (overflow, protrusion, subsidence, missing, damage, surrounding pavement damage and subsidence), rainwater collection wells (missing, damage, obstruction, protrusion, surrounding pavement subsidence or water accumulation), pavement subsidence above the pipeline, cracks and water accumulation, etc.

[0055] The pavement conditions evaluated in the traffic and auxiliary facilities evaluation module include markings, signs, signal lights, sound barriers, separation strips, guardrails and barriers, billboards, street trees, etc.

[0056] The above road condition feedback unit includes a road management feedback module and a user feedback module.

[0057] The road management feedback module communicates with the road management department, receives the road condition evaluation results output by the system evaluation module, and issues a complete road condition evaluation report, identifies the accurate positions and damage degrees of different damage types, and gives the evaluation results to the road management department according to the road maintenance specifications; wherein, for the road management department, the single damage type, small area damage, large area damage, etc. are comprehensively considered, and the road condition evaluation report is issued in combination with the road maintenance specifications; for example, the damage of asphalt pavement includes crack type, deformation type, loose type and other type, the crack type is divided into linear crack, net crack and crack, the linear crack is single damage, when the linear crack crack width is less than 10mm, it is small area damage, and when the crack width is greater than 10mm, it is large area damage.

[0058] The user feedback module communicates with the vehicle receiving end, combines the road condition evaluation result output by the system evaluation module, is loaded into the vehicle-mounted navigation system of the vehicle receiving end, and feeds back the road condition in real time. Among them, for the vehicle receiving end, that is, for the vehicle receiving end, combined with the evaluation result, as a navigation APP plug-in, the road condition in front is prompted in real time on the navigation APP; thus, the vehicle receiving end user can make a speed reduction or avoidance instruction in a timely manner according to the road condition feedback, and the driving safety and comfort of the vehicle receiving end driver user are ensured.

[0059] To sum up, the evaluation system for detecting road conditions by the vehicle-mounted laser radar is arranged, the new energy intelligent driving vehicle is combined with the road condition detection equipment, the dynamic collection of technical parameters of road conditions can be realized, and the technical parameters of road conditions are converted into prompt slogans and voice prompts. Among them, after a large area of damage is collected, the avoidance instruction can be output according to the research and judgment of the system evaluation module, and is synchronously transmitted to the vehicle receiving end or the driver user of the vehicle receiving end, and is recognized by the intelligent driving vehicle algorithm or avoided by the driver user in time; when a small area of damage or a single damage is collected, the small area of damage or the single damage can be used as a system prompt to trigger an early speed reduction instruction. Thus, the problem of high cost of the special road condition detection equipment is effectively solved, the road conditions are collected by the vehicle-mounted laser radar, and the road conditions are fed back to the road management department and the vehicle receiving end in real time, the road condition detection efficiency of the road management department is improved, and the driving safety of the driver user of the vehicle receiving end is ensured.

[0060] Embodiment 2: as Figure 3 shown, the embodiment of the application discloses an evaluation method for detecting road operation conditions by using a vehicle-mounted laser radar: comprising:

[0061] S101, collecting road surface condition data in real time;

[0062] S102, receiving the road surface condition data, processing the data, and completing road condition parameter identification, wherein the data processing includes data denoising, missing value filling, keyword extraction and data fusion;

[0063] S103, constructing a road condition environment model according to the road condition parameters, evaluating the damage degree of the road condition disaster according to the road condition environment, and outputting a road condition evaluation result;

[0064] S104, receiving the road condition evaluation result, and feeding back to the vehicle receiving end and the road management department, reminding the vehicle driver to avoid the disaster damaged road section, and analyzing by the road management department whether the disaster damaged road section needs to be repaired.

[0065] In the above step S103, the road condition environment model is constructed according to the road condition parameters, the damage degree of the road condition disaster is evaluated according to the road condition environment, and the road condition evaluation result is output, comprising:

[0066] According to the road disaster identification under different road condition parameters, the algorithm is selected, the accuracy of the algorithm is verified, the road condition environment model is constructed, and the road condition environment module is used to evaluate different road conditions;

[0067] According to different road conditions, the damage degree of road disasters is evaluated according to the existing specification, and different road disaster prompt labels and voice prompts are input according to the preset evaluation labels, and the road evaluation result is output;

[0068] The road evaluation result is received and fed back to the road management department and the vehicle receiving end in time; wherein, the feedback road condition result includes that the road appears abnormal subsidence, cavity; the road appears obstacles; the road surface appears more than 50mm of wrong table; the manhole cover, rainwater inlet grille is lost or seriously damaged; the road surface appears serious water accumulation, icing, etc.

[0069] Embodiment 3: The embodiment of the application discloses a storage medium: the storage medium has a computer program readable by a computer stored thereon, and the computer program is set to execute the evaluation method for detecting road operation conditions by using a vehicle-mounted laser radar when running.

[0070] The above-mentioned storage medium can include but is not limited to: U disk, read-only memory, mobile hard disk, magnetic disk or optical disk and various computer program storage media.

[0071] Embodiment 4: The embodiment of the application discloses an electronic device, including a processor and a memory, the memory has a computer program stored therein, and the computer program is loaded and executed by the processor to realize the evaluation method for detecting road operation conditions by using a vehicle-mounted laser radar.

[0072] The above-mentioned electronic device further includes a transmission device and an input-output device, wherein the transmission device and the input-output device are connected with the processor.

[0073] The above-mentioned processor can be a central processing unit CPU, a general-purpose processor, a digital signal processor DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logic blocks, modules and circuits described in combination with the disclosure content. It can also be a combination of computing functions, such as one or more microprocessor combinations, DSP and microprocessor combinations, etc. The memory can include but is not limited to: U disk, read-only memory, mobile hard disk, magnetic disk or optical disk and various computer program storage media.

[0074] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied thereon. The procedures described in this application can be implemented as a computer program product, using any of a variety of computer languages, such as Java, JavaScript, and the like.

[0075] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0076] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0077] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0078] Embodiment 5: The embodiment of the application discloses a terminal, comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the program comprising instructions for performing steps of the evaluation method for detecting road operating conditions using a vehicle-mounted laser radar.

Claims

1. An evaluation system for detecting road operating conditions using a vehicle-mounted laser radar, characterized by, The system comprises a vehicle-mounted laser radar collection unit, a 3D cloud data unit, a road operation condition assessment unit and a road condition feedback unit. The vehicle-mounted laser radar collection unit collects road surface condition data in real time. The 3D cloud data unit receives the road surface condition data and processes the data to complete road condition parameter identification, wherein the data processing comprises data denoising, missing value filling, keyword extraction and data fusion. The road operation condition assessment unit constructs a road condition environment model according to the road condition parameters and assesses the damage degree of road condition disasters according to the road condition environment to output a road condition assessment result; wherein the damage degree comprises single damage, small-area damage and large-area damage. The road condition feedback unit receives the road condition assessment result and feeds back to a vehicle receiving end and a road management department to remind a vehicle driver to avoid a disaster-damaged road section and to analyze whether the disaster-damaged road section needs to be repaired by the road management department. The 3D cloud data unit comprises a data stream processing module and a road condition parameter identification module. The data stream processing module denoises, fills in missing values, extracts keywords and fuses data for the received road surface condition data and outputs the processed data to the road condition parameter identification module. The road condition parameter identification module completes road condition parameter identification according to the processed data, wherein the road condition parameters comprise road surface obstacles, road surface defects, road surface cracks and road surface settlement.

2. The evaluation system for detecting road operating conditions using a vehicle-mounted laser radar according to claim 1, wherein The road operation condition assessment unit comprises a construction environment model module, a system assessment module and a road condition assessment result module. The construction environment model module selects an algorithm according to road disaster identification under different road condition parameters, verifies the accuracy of the algorithm, completes construction of a road condition environment model and evaluates different road condition types through a road condition environment module. The system assessment module assesses the damage degree of road condition disasters according to different road condition types and current specifications, inputs different road condition disaster prompt marks and voice prompts according to preset assessment labels and outputs a road condition assessment result; wherein the damage degree comprises single damage, small-area damage and large-area damage. The road condition assessment result module receives the road condition assessment result and outputs the road condition assessment result to the road condition feedback unit; wherein the road condition assessment result comprises that a road appears abnormal settlement or cavity, a road appears obstacles, a road surface appears a fault greater than 50 mm, a manhole cover or a rainwater grate is lost or seriously damaged and a road surface appears serious water accumulation or icing.

3. The evaluation system for detecting road operating conditions using a vehicle-mounted laser radar according to claim 2, wherein The system assessment module comprises a roadbed and pavement assessment module, a bridge assessment module, a tunnel assessment module, a pipeline assessment module and a traffic and accessory facility assessment module.

4. The evaluation system for detecting road operating conditions using a vehicle-mounted laser radar according to claim 1, wherein The road condition feedback unit comprises a road management feedback module and a user feedback module. The road management feedback module communicates with a road management department, receives the road condition assessment result output by the system assessment module, issues a perfect road condition assessment report, identifies the accurate positions and damage degrees of different damage types and gives the assessment result to the road management department according to road maintenance specifications. The user feedback module communicates with a vehicle receiving end, combines the road condition assessment result output by the system assessment module, is loaded onto a vehicle-mounted navigation system of the vehicle receiving end and feeds back road condition information in real time.

5. An evaluation method for detecting a road operating condition using a vehicle-mounted laser radar, characterized by, The system comprises: collecting road surface condition data in real time. Receiving road surface condition data and processing the data to complete road condition parameter identification, wherein the data processing includes data denoising, missing value filling, keyword extraction and data fusion; According to the road condition parameter, a road condition environment model is constructed, and according to the road condition environment, the damage degree of the road condition disaster is evaluated, and the road condition evaluation result is output; wherein the damage degree of the disaster includes single damage, small area damage and large area damage; Receiving the road condition evaluation result and feeding back to the vehicle receiving end and the road management department, reminding the vehicle driver to avoid the disaster damaged road section, and analyzing whether the disaster damaged road section needs to be repaired by the road management department; Denoising, missing value filling, keyword extraction and data fusion are performed on the received road surface condition data, and the processed data is output; According to the processed data, road condition parameter identification is completed, wherein the road condition parameters include road obstacles, road defects, road cracks and road subsidence.

6. The method of claim 5, wherein the method further comprises: The road condition environment model is constructed according to the road condition parameter, and the damage degree of the road condition disaster is evaluated according to the road condition environment, and the road condition evaluation result is output, including: According to the identification of road disasters under different road conditions, the algorithm is selected, the accuracy of the algorithm is verified, the road condition environment model is constructed, and the road condition environment module is evaluated according to different road conditions; According to different road conditions, the damage degree of road disaster is evaluated according to the existing specification, and different road disaster prompt signs and voice prompts are input according to the preset evaluation label, and the road condition evaluation result is output; Receiving the road condition evaluation result and feeding back to the road management department and the vehicle receiving end in time; wherein the feedback road condition result includes road abnormal subsidence or cavity; road obstacle; road surface more than 50mm; manhole cover or rainwater grate missing or seriously damaged; and road surface serious waterlogging or icing.

7. A storage medium, characterized by The storage medium has a computer program readable by a computer, and the computer program is set to run and execute the road operation condition evaluation method using the vehicle-mounted laser radar as claimed in any one of claims 5 to 6.

8. A terminal, characterized by comprising: Including processor, memory, communication interface, and one or more programs, the one or more programs are stored in the memory and configured to be executed by the processor, the program includes instructions for executing steps in the road operation condition evaluation method using the vehicle-mounted laser radar as claimed in any one of claims 5 to 6.

9. An electronic device, comprising: Including processor and memory, the memory has a computer program, the computer program is loaded and executed by the processor to realize the road operation condition evaluation method using the vehicle-mounted laser radar as claimed in claims 5 to 6.

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