Digital road marking processing method and system for intelligent driving

Abstract

The invention discloses a digital road marking processing method and system for intelligent driving, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining road marking data, and constructing a cloud digital marking platform; acquiring vehicle surrounding information acquired by a vehicle-mounted sensor in real time, and performing data fusion processing in combination with marking information of a positioning system to obtain a fused and optimized digital marking result; and encrypting and transmitting the road marking data of the cloud digital marking platform to the vehicle-mounted driving system, matching the decrypted road marking data with the fused and optimized digital marking result, and generating a driving instruction according to the matching result. By fusing the real-time information of the vehicle-mounted sensor and the positioning marking information, the digital marking result is optimized, the data real-time performance and the scene adaptability are improved, the dynamic environment around the vehicle is accurately reflected, the problem that traditional visual recognition is affected by environmental factors is effectively solved, and traffic safety and efficiency improvement are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and particularly to a digital road marking processing method and system for intelligent driving. Background Art

[0002] Intelligent driving is the product of the combination of artificial intelligence and new energy technologies. With the popularization of new energy vehicles, intelligence has become a new focus, and the number of L2+-level autonomous driving vehicles is increasing rapidly. At the same time, as an important application field of AI technology, intelligent driving provides a wide range of application scenarios.

[0003] The key technology of intelligent driving lies in the extraction of road marking information during vehicle driving. Existing road marking recognition methods mainly rely on visual sensors to extract lane features through image processing technologies such as edge detection. However, this method is easily affected by external factors such as light and weather, resulting in unstable recognition results.

[0004] In view of this, a digital road marking processing method and system for intelligent driving are needed. Summary of the Invention

[0005] Aiming at the problem that the extraction of road marking information in the prior art is easily affected by the environment, the present invention provides a digital road marking processing method and system for intelligent driving, which can improve the accuracy of road marking recognition and reduce misjudgment caused by environmental changes. The specific technical solutions are as follows:

[0006] A digital road marking processing method for intelligent driving includes:

[0007] Obtain road marking data and build a cloud digital marking platform;

[0008] Obtain the information around the vehicle collected by on-vehicle sensors in real time, and perform data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization;

[0009] Encrypt and transmit the road marking data of the cloud digital marking platform to the in-vehicle driving system, match the decrypted road marking data with the digitally marked result after fusion and optimization, and generate a driving instruction according to the matching result.

[0010] Preferably, the obtaining of road marking data and building of the cloud digital marking platform includes:

[0011] Obtain the basic information of road markings collected during the spraying and marking process by a road marking machine and upload it to the cloud;

[0012] Obtain the marking position data in the positioning system;

[0013] Based on the basic information of road markings and the marking position data, a digital marking map is sorted out and generated, and a cloud digital marking platform is constructed.

[0014] Preferably, the basic information of road markings collected during the spraying and marking process by the road marking machine includes:

[0015] Determine the basic information of the target road markings that the road marking machine needs to spray, and obtain the key point collection instruction for the target road markings according to the basic information of the target road markings;

[0016] Collect the key point information during the construction spraying of the target road markings according to the key point collection instruction to obtain the basic information of the road markings.

[0017] Preferably, the vehicle surrounding information collected by the vehicle-mounted sensor in real time is obtained, and data fusion processing is carried out in combination with the marking information of the positioning system. The obtained fused and optimized digital marking result includes:

[0018] Obtain the road data collected by the vehicle-mounted camera in real time, and perform analysis and processing to obtain the visual perception data of the road markings;

[0019] Obtain the road data collected by the vehicle-mounted radar in real time, and perform analysis and processing to obtain the radar detection data of the road markings;

[0020] Perform fusion processing according to the visual perception data and radar detection data of the road markings, and in combination with the marking data of the positioning system of the current road to obtain the fused and optimized digital marking result.

[0021] Preferably, performing fusion processing according to the visual perception data and radar detection data of the road markings, and in combination with the marking data of the positioning system of the current road, the obtained fused and optimized digital marking result includes:

[0022] Use the coordinate transformation algorithm to transform the visual perception data, radar detection data and marking data of the positioning system into a unified global coordinate system;

[0023] Extract the road marking edge features in the visual perception data and the road marking point cloud features in the radar detection data, and fuse the extracted features in the unified global coordinate system to obtain the digital marking result of the fused feature information.

[0024] Preferably, encrypting and transmitting the road marking data of the cloud digital marking platform to the vehicle-mounted driving system, and matching the decrypted road marking data with the fused and optimized digital marking result, and generating a driving instruction according to the matching result includes:

[0025] Encrypt the road marking data of the cloud digital marking platform through a preset key, and transmit the encrypted road marking data to the vehicle-mounted driving system;

[0026] When the encrypted road marking data is transmitted to the in-vehicle driving system for key verification, the decrypted road marking data is obtained;

[0027] Match the decrypted road marking data with the result of the fused and optimized digital road marking. If the matching result reaches the preset similarity threshold, generate a driving instruction according to the result of the fused and optimized digital road marking.

[0028] Preferably, a digital road marking processing method for intelligent driving further includes:

[0029] If the matching result does not reach the preset similarity threshold, issue a warning message to exit intelligent driving.

[0030] A digital road marking processing system for intelligent driving, which is applied to the foregoing method, includes:

[0031] A marking platform construction unit, configured to obtain road marking data and construct a cloud digital marking platform;

[0032] A marking fusion processing unit, configured to obtain the information around the vehicle collected by the in-vehicle sensor in real time, and perform data fusion processing in combination with the marking information of the positioning system to obtain the result of the fused and optimized digital road marking;

[0033] A data encryption and decryption unit, configured to encrypt and transmit the road marking data of the cloud digital marking platform to the in-vehicle driving system, match the decrypted road marking data with the result of the fused and optimized digital road marking, and generate a driving instruction according to the matching result.

[0034] A computer-readable storage medium, the computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the foregoing digital road marking processing method for intelligent driving.

[0035] A processor, the processor is used to run a program, wherein when the program runs, it executes the foregoing digital road marking processing method for intelligent driving.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] A method for processing digital road markings for intelligent driving according to the present invention obtains road marking data and constructs a cloud digital marking platform; obtains information around the vehicle collected in real time by on-vehicle sensors, and performs data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization; encrypts and transmits the road marking data of the cloud digital marking platform to the on-vehicle driving system, and matches the decrypted road marking data with the digitally marked result after fusion and optimization, and generates a driving instruction according to the matching result. By constructing a cloud digital marking platform, integrating road marking data, providing basic data support, and fusing real-time information of on-vehicle sensors and positioning marking information, the present invention optimizes the digitally marked result, improves data real-time performance and scene adaptability, accurately reflects the dynamic environment around the vehicle, effectively overcomes the problem that traditional visual recognition is affected by environmental factors, provides more reliable and stable lane marking information support for intelligent driving, and promotes traffic safety and efficiency improvement. At the same time, encrypted transmission ensures data security and avoids transmission risks, and the mechanism of generating driving instructions by data matching enables the vehicle to make decisions based on accurate comparison results, significantly improving the safety and decision-making accuracy of intelligent driving, realizing a full-process closed loop from data integration, optimization to safe application, and promoting the intelligence and stability of the driving system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0039] Figure 1 It is a flowchart of a method for processing digital road markings for intelligent driving according to the present invention.

[0040] Figure 2 It is a flowchart of an embodiment of a method for processing digital road markings for intelligent driving according to the present invention.

[0041] Figure 3 It is a flowchart of another embodiment of a method for processing digital road markings for intelligent driving according to the present invention.

[0042] Figure 4 It is a schematic diagram of the principle of a system for processing digital road markings for intelligent driving according to the present invention.

[0043] Figure 5 It is a schematic diagram of a digital marking map according to an embodiment of the present invention.

[0044] Figure 6 It is a schematic diagram of road marking information according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0047] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0048] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0049] The following embodiments refer to Figures 1 to 6 。

[0050] The embodiments of the present application provide a digital road marking processing method for intelligent driving, including:

[0051] S01. Obtain road marking data and build a cloud digital marking platform;

[0052] Obtain road marking data (such as lane line coordinates, marking types, etc.) through channels such as surveying and mapping equipment and road construction systems, and combine the high-precision positioning data of the positioning system to record the key point positions of each marking and assign a unique spatial attribute to each marking, including but not limited to geographical location coordinates, shape parameters, etc. Build a digital marking platform based on the cloud server to store and manage the road marking data and build a cloud road marking digital platform covering road marking information.

[0053] S02. Obtain the information around the vehicle collected by the vehicle-mounted sensor in real time, and perform data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization;

[0054] By using on-vehicle sensors (such as cameras, millimeter-wave radars, lidar, etc.) to collect real-time information on the vehicle's surrounding environment (such as real-time lane line visual images, obstacle distances), and combining the marking position information obtained by the positioning system (such as GPS, Beidou), data fusion processing is carried out through algorithms, and finally the fused and optimized digital marking results are output to accurately obtain the marking environment in the vehicle's real-time driving scenario.

[0055] S03. Encrypt and transmit the road marking data of the cloud digital marking platform to the in-vehicle driving system, match the decrypted road marking data with the fused and optimized digital marking results, and generate driving instructions according to the matching results.

[0056] By establishing a data transmission protocol between the cloud and the in-vehicle driving system, through an encrypted data communication protocol, privacy protection during data transmission is ensured. The road marking data of the cloud digital marking platform is securely transmitted through an encryption algorithm (such as AES encryption) to ensure that the data is not stolen or tampered with during transmission. After receiving the data, the in-vehicle driving system decrypts the data, matches and analyzes it with the fused and optimized digital marking results (such as comparing the marking position deviation, lane change trend), and generates driving instructions according to the matching results to achieve precise decision-making for intelligent driving.

[0057] The present invention constructs a cloud digital marking platform, integrates road marking data, and provides basic data support; fuses real-time information of on-vehicle sensors and positioning marking information to optimize digital marking results, improves data real-time performance and scene adaptability, accurately reflects the dynamic environment around the vehicle, effectively overcomes the problem that traditional visual recognition is affected by environmental factors, provides more reliable and stable lane marking information support for intelligent driving, and promotes traffic safety and efficiency improvement. And encrypting the transmission to ensure data security and avoid transmission risks, while the mechanism of data matching to generate driving instructions enables the vehicle to make decisions based on precise comparison results, significantly improving the safety and decision-making accuracy of intelligent driving, realizing a full-process closed-loop from data integration, optimization to safe application, and promoting the intelligence and stability of the driving system.

[0058] Specifically, in a preferred embodiment of the present application, the obtaining of road marking data and the construction of the cloud digital marking platform include:

[0059] S11. Obtain the basic information of road markings collected during the spraying and marking process by the road marking machine and upload it to the cloud;

[0060] During the construction stage of the road marking machine, basic data during the marking spraying process is collected in real time, including information such as marking type (such as lane dividing line, stop line), color (white, yellow, etc.), width, etc.

[0061] Specifically, the basic information of the road marking obtained during the spraying and marking process by the road marking machine includes:

[0062] Determine the basic information of the target road marking that the road marking machine needs to spray, and obtain the key point acquisition instruction for the target road marking according to the basic information of the target road marking;

[0063] Collect the key point information during the construction spraying of the target road marking according to the key point acquisition instruction to obtain the basic information of the road marking.

[0064] In this embodiment, by surveying the existing roads, the marking information of each lane is obtained to improve the accuracy of obtaining the marking information. This embodiment also provides a road marking machine with marking digitization. The road marking machine system includes a road marking digitization unit for determining the basic information of the target road marking, issuing the key point acquisition instruction for the target road marking according to the basic information of the target road marking, and storing the basic information of the target road marking and the key point acquisition instruction information; a road marking positioning unit for receiving the key point acquisition instruction for the target road marking, collecting the key point information during the construction spraying of the target road marking according to the key point acquisition instruction, and uploading the collected key point information to the road marking digitization unit; a road marking spraying unit for performing the construction spraying of the target road marking according to the preset road marking drawing.

[0065] S12. Obtain the marking position data in the positioning system;

[0066] Through the positioning system (such as Beidou, GPS or high-precision map positioning technology), obtain the precise coordinate information of the road marking in the geographical space, including the longitude and latitude of the starting point, ending point, and turning point of the marking, as well as the spatial position data of the marking extending along the road. For example, use Beidou positioning equipment to collect the marking position data in real time. In the process of realizing precise positioning by Beidou / RTK technology, determine the number and set positions of the reference stations, then use the set reference stations as the basis for data processing to calculate the positioning accuracy, and then send the error correction data to the system. Finally, the user part performs real-time error correction on the obtained positioning information to obtain accurate positioning information.

[0067] S13. According to the basic information of the road marking and the marking position data, organize and generate a digital marking map and build a cloud digital marking platform.

[0068] Based on the basic information (attribute data) and marking position data (spatial coordinates) of road markings obtained in the first two steps, they are integrated through a data processing algorithm. For example, attributes such as marking type and color are associated with specific geographical coordinates to draw a digital marking map containing rich semantic information. Finally, relying on the cloud server, a digital marking platform is built to store, manage, and publish map data, providing standardized and high-precision road marking data support for the intelligent driving system.

[0069] In this embodiment, GIS is used to process road marking data, and various types of marking information are sorted out to generate a digital marking map. This map contains the position, type, and status information of all markings. Each marking is assigned a unique identifier, and its relevant attributes such as position coordinates, length, and width are stored. The markings collected by the road marking machine and the Beidou positioning module are visualized in GIS. Based on the generated digital marking map (as Figure 5 shown), a cloud digital marking platform is established, which contains comprehensive marking information. This background can provide updated road marking information to support the query and use of the intelligent driving system. As Figure 6 shown, each road is assigned a unique id code, as well as basic information such as name (road name), fclass (road type), and ref (road number). At the same time, the road is judged, for example: oneway (whether it is a one-way street), bridge (whether it is a bridge), tunnel (whether it is a tunnel), with two values F and T, where F represents no and T represents yes.

[0070] Specifically, in a preferred implementation manner of the present application, obtaining the information around the vehicle collected by the on-vehicle sensor in real time and performing data fusion processing in combination with the marking information of the positioning system, the obtained digital marking result after fusion optimization includes:

[0071] S21. Obtain the road data collected by the on-vehicle camera in real time and perform analysis and processing to obtain the visual perception data of the road markings;

[0072] On-vehicle cameras are important visual sensors in intelligent driving systems, capable of capturing real-time images of road scenes around the vehicle, such as in front of and on the sides of the vehicle. After obtaining this real-time road data collection, it is necessary to analyze and process it. Operations such as filtering and noise reduction are performed on the collected original images; image processing and computer vision algorithms are used to extract features related to road markings from the preprocessed images, such as information on the edges, colors, and textures of the markings. By identifying these features, the approximate position and type of the road markings can be determined. The extracted features are converted into digital-form data, for example, converting the edge information of the markings into coordinate data. Then, these data are analyzed to judge features such as the continuity and curvature of the road markings, and finally, visual perception data of the road markings are obtained. Road images are captured in real time by on-vehicle cameras. The images first undergo preprocessing and then feature extraction and target localization; different post-processing operations are performed on the prediction results according to preset categories, and the post-processed results are fused and calibrated with the digital data of the road markings to obtain real-time and accurate road perception information.

[0073] Among them, the road marking lines are classified into categories:

[0074] (1) Road digital and text information: digital and text signs such as vehicle speed signs and road text;

[0075] (2) Road data: lane lines, stop lines, lane arrows, zebra crossings, etc.;

[0076] (3) Road fixed objects: non-digital traffic signs, traffic lights, utility poles, manhole covers, etc.

[0077] S22. Obtain the real-time road data collected by the on-vehicle radar, and perform analysis and processing to obtain the radar detection data of the road markings;

[0078] On-vehicle radars (such as millimeter-wave radars and lidar) detect the objects and environmental information around the vehicle in real time by emitting electromagnetic waves and receiving reflected waves. After obtaining the real-time road data collected by the radar, radar signal processing algorithms are used to detect the target objects on the road, including road markings. By continuously monitoring and tracking the target objects, information such as their positions and speeds is obtained. Features related to road markings, such as the distance and angle of the markings, are extracted from the detected target object data. These features are analyzed to determine the specific position and shape of the road markings, and the radar detection data of the road markings are obtained.

[0079] S23. According to the visual perception data and radar detection data of the road markings, and in combination with the marking data of the current road's positioning system, perform fusion processing to obtain the fused and optimized digital marking results.

[0080] After obtaining the visual perception data and radar detection data of road markings, combined with the marking data provided by the positioning system of the current road (such as GPS, high-precision map), fusion processing is carried out: Since the data of different sensors may have differences in coordinate system and time, it is necessary to align the visual perception data, radar detection data and the marking data of the positioning system so that they can be compared and fused in the same coordinate system and time scale. And the data of different sensors are comprehensively utilized through Kalman filtering. The aligned data is input into the fusion algorithm for processing to obtain the fused and optimized digital marking result.

[0081] In this embodiment, the in-vehicle camera can obtain the visual features of road markings, and the radar can obtain the spatial distance information. Combined with the accurate position of the positioning system, the road marking information is more accurate and comprehensive, reducing the error of a single sensor. At the same time, different sensors have their own advantages in different scenarios. After multi-source fusion, the intelligent driving system can stably identify the markings under various lighting and weather conditions. The fused and optimized digital marking result effectively improves the overall performance and safety of the intelligent driving system.

[0082] Specifically, according to the visual perception data and radar detection data of road markings, and combined with the marking data of the positioning system of the current road for fusion processing, the obtained fused and optimized digital marking result includes:

[0083] Using the coordinate transformation algorithm to transform the visual perception data, radar detection data and the marking data of the positioning system into a unified global coordinate system;

[0084] Extract the edge features of road markings in the visual perception data and the point cloud features of road markings in the radar detection data, and fuse the extracted features in the unified global coordinate system to obtain the digital marking result of the fused feature information.

[0085] By fusing the visual perception data, radar detection data and the marking data of the positioning system, the advantages of each data source can be fully utilized to make up for the deficiencies of single data. For example, the visual perception data may be limited under insufficient light or bad weather conditions, while the radar detection data is not affected by light and has a certain penetration ability. The combination of the two can obtain more comprehensive and accurate road marking information.

[0086] Among them, the data obtained by different sensors may have errors and uncertainties. Through fusion processing, they can be mutually verified and corrected to improve the overall accuracy and reliability of the data. For example, after coordinate transformation to a unified global coordinate system, the position and shape of road markings can be more accurately located and identified. The fused digital marking results not only contain the edge features of road markings in visual perception, but also combine the point cloud features of road markings in radar detection, providing more comprehensive road environment information for vehicles. Especially in complex road scenarios such as curves, tunnels, and multi-lane intersections, a single sensor may be difficult to accurately identify and locate road markings, while fusion processing can better address these challenges and improve the vehicle's perception ability and adaptability to complex environments.

[0087] Specifically, in a preferred embodiment of the present application, the steps of encrypting and transmitting the road marking data of the cloud digital marking platform to the in-vehicle driving system, and matching the decrypted road marking data with the fused and optimized digital marking results, and generating driving instructions according to the matching results include:

[0088] Encrypt the road marking data of the cloud digital marking platform with a preset key, and transmit the encrypted road marking data to the in-vehicle driving system;

[0089] Perform an encryption operation on the road marking data in the cloud digital marking platform by using a preset key. The encryption algorithm can be a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA). The purpose of encryption is to prevent data from being stolen or tampered with during transmission, and to ensure the security and integrity of the data. After encryption, the encrypted road marking data is transmitted to the in-vehicle driving system through the network.

[0090] When the encrypted road marking data is transmitted to the in-vehicle driving system, perform key verification to obtain the decrypted road marking data;

[0091] When the encrypted road marking data is transmitted to the in-vehicle driving system, the system will perform key verification. The in-vehicle driving system needs to use the same key as the cloud to attempt to decrypt the data. Only when the key verification passes, that is, when the correct key can successfully decrypt the data, can the decrypted road marking data be obtained. If the key verification fails, it means that there may be problems with the data during transmission or there is an illegal access situation.

[0092] Match the decrypted road marking data with the fused and optimized digital marking results. If the matching result reaches a preset similarity threshold, generate driving instructions according to the fused and optimized digital marking results.

[0093] If the matching result does not reach the preset similarity threshold, issue a warning message to exit intelligent driving.

[0094] Match the decrypted road marking data with the digitally marked line results after fusion and optimization. The matching process compares the features of the two, such as information about the position, shape, type, etc. of the marked lines. If the matching result reaches the preset similarity threshold, it means that the decrypted road marking data is highly consistent with the results after real-time fusion and optimization by the in-vehicle system. At this time, the data can be considered reliable. Based on the digitally marked line results after fusion and optimization, combined with the decision-making algorithm of intelligent driving, corresponding driving instructions are generated, such as lane keeping, lane changing, deceleration, etc.

[0095] If the matching result does not reach the preset similarity threshold, this may indicate that an error occurred during data transmission, or there are significant differences between the cloud data and the in-vehicle real-time data, and there may be problems such as data inconsistency and sensor failures. To ensure driving safety, the system will issue a warning message to exit intelligent driving, reminding the driver to take over the vehicle manually.

[0096] In this embodiment, by encrypting the transmission and verifying the key of the road marking data, it effectively prevents the leakage and tampering of data during transmission, ensures the security of the key data relied on by the intelligent driving system, and reduces the security risks caused by data attacks. Matching the decrypted road marking data with the digitally marked line results after fusion and optimization, and generating driving instructions only when the matching degree reaches a certain threshold, can ensure that the driving instructions are generated based on accurate and reliable data, improve the accuracy of intelligent driving decisions, and reduce wrong decisions caused by data errors. When the matching result does not reach the preset threshold, a warning message to exit intelligent driving is issued in a timely manner, reminding the driver to take over the vehicle manually, avoiding continuing to execute the intelligent driving function under unreliable data, thereby reducing the possibility of traffic accidents.

[0097] This application embodiment also provides a digital road marking processing system for intelligent driving, which is applied to the foregoing method, including:

[0098] A marked line platform construction unit, configured to obtain road marking data and construct a cloud digital marked line platform;

[0099] A marked line fusion processing unit, configured to obtain the information around the vehicle collected in real time by the in-vehicle sensor, and perform data fusion processing in combination with the marked line information of the positioning system to obtain the digitally marked line results after fusion and optimization;

[0100] A data encryption and decryption unit, configured to encrypt and transmit the road marking data of the cloud digital marked line platform to the in-vehicle driving system, match the decrypted road marking data with the digitally marked line results after fusion and optimization, and generate driving instructions according to the matching results.

[0101] The function explanations of each unit in this embodiment are the same as those of a digital road marking processing method for intelligent driving, and the technical effects are the same, so they will not be repeated here.

[0102] The embodiment of the present application also provides a computer-readable storage medium, which includes a stored program. When the program runs, it controls the device where the computer-readable storage medium is located to execute the foregoing digital road marking processing method for intelligent driving.

[0103] The technical effect of this embodiment is the same as that of an embodiment of a digital road marking processing method for intelligent driving, and will not be repeated here.

[0104] The present invention can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0105] The embodiment of the present application also provides a processor, which is used to run a program. When the program runs, it executes the foregoing digital road marking processing method for intelligent driving.

[0106] The technical effect of this embodiment is the same as that of an embodiment of a digital road marking processing method for intelligent driving, and will not be repeated here.

[0107] The processor in this embodiment can be a central processing unit (CPU), a controller, a microcontroller, or other data processing chips.

[0108] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0109] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0110] In addition, each functional unit in various embodiments of the present invention may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of various embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A digital road marking processing method for intelligent driving, characterized in that Including: Obtain road marking data and construct a cloud digital marking platform; Obtain the information around the vehicle collected by on-vehicle sensors in real time, and perform data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization; Encrypt and transmit the road marking data of the cloud digital marking platform to the in-vehicle driving system, match the decrypted road marking data with the digitally marked result after fusion and optimization, and generate a driving instruction according to the matching result.

2. The digital road marking processing method for intelligent driving according to claim 1, wherein The obtaining of road marking data and constructing a cloud digital marking platform includes: Obtain the basic information of road markings collected by a road marking machine during the process of spraying and marking, and upload it to the cloud; Obtain the marking position data in the positioning system; According to the basic information of road markings and the marking position data, organize and generate a digital marking map, and construct a cloud digital marking platform.

3. A digital road marking processing method for intelligent driving according to claim 2, characterized in that The obtaining of the basic information of road markings collected by a road marking machine during the process of spraying and marking includes: Determine the basic information of the target road markings to be sprayed by the road marking machine, and obtain the key point collection instruction for the target road markings according to the basic information of the target road markings; Collect the key point information during the construction spraying of the target road markings according to the key point collection instruction to obtain the basic information of the road markings.

4. A digital road marking processing method for intelligent driving according to claim 1, characterized in that, The obtaining of the information around the vehicle collected by on-vehicle sensors in real time, and performing data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization includes: Obtain the road data collected by an on-vehicle camera in real time, and perform analysis and processing to obtain the visual perception data of the road markings; Obtain the road data collected by an on-vehicle radar in real time, and perform analysis and processing to obtain the radar detection data of the road markings; According to the visual perception data and radar detection data of the road markings, and in combination with the marking data of the positioning system of the current road, perform fusion processing to obtain a digitally marked result after fusion and optimization.

5. A digital road marking processing method for intelligent driving according to claim 4, characterized in that According to the visual perception data and radar detection data of the road markings, and in combination with the marking data of the positioning system of the current road, perform fusion processing to obtain a digitally marked result after fusion and optimization includes: Use a coordinate transformation algorithm to transform the visual perception data, radar detection data, and marking data of the positioning system into a unified global coordinate system; Extract the road marking edge features in the visual perception data and the road marking point cloud features in the radar detection data, and fuse the extracted features in the unified global coordinate system to obtain a digitally marked result with fused feature information.

6. A digital road marking processing method for intelligent driving according to claim 1, characterized in that, The encrypting and transmitting the road marking data of the cloud digital marking platform to the in-vehicle driving system, matching the decrypted road marking data with the digitally marked result after fusion and optimization, and generating a driving instruction according to the matching result includes: Encrypt the road marking data of the cloud digital marking platform through a preset key, and transmit the encrypted road marking data to the in-vehicle driving system; When the encrypted road marking data is transmitted to the in-vehicle driving system, perform key verification to obtain the decrypted road marking data; Match the decrypted road marking data with the digitally marked result after fusion and optimization. If the matching result reaches a preset similarity threshold, generate a driving instruction according to the digitally marked result after fusion and optimization.

7. A method for processing digital road markings for intelligent driving according to claim 6, characterized in that, Further included are: If the matching result does not reach the preset similarity threshold, an exit warning message for intelligent driving is issued.

8. A digital road marking processing system for intelligent driving, characterized in that, Applied to the method according to any one of claims 1 to 7, it includes: A marking platform construction unit, configured to obtain road marking data and construct a cloud digital marking platform; A marking fusion processing unit, configured to obtain information around the vehicle collected in real time by an in-vehicle sensor, and perform data fusion processing in combination with the marking information of the positioning system to obtain a digitally marked result after fusion and optimization; A data encryption and decryption unit, configured to encrypt and transmit the road marking data of the cloud digital marking platform to the in-vehicle driving system, and match the decrypted road marking data with the digitally marked result after fusion and optimization, and generate a driving instruction according to the matching result.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method for processing digital road markings for intelligent driving according to any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run the program, wherein when the program runs, it executes the method for processing digital road markings for intelligent driving according to any one of claims 1 to 7.

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