Visual distance triangle traffic potential safety hazard detection method and system based on point cloud data

Abstract

The invention provides a sight distance triangle traffic potential safety hazard detection method and system based on point cloud data, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting the point cloud data of a road intersection, and obtaining the road speed limit data at the same time; preprocessing the point cloud data to generate a vector road network, and associating the road speed limit data into road network attributes; automatically generating a sight distance triangular range corresponding to the lane speed according to the road speed limit data in the road network attribute; lifting the sight distance triangular plane to a set height above the ground, scanning the point cloud data in the elevation plane through ray collision detection, and identifying an obstacle with the height greater than or equal to the set height and the spatial position of the obstacle; generating an early warning information list containing the position and the type of the obstacle; and outputting a standardized hidden danger identification report. According to the invention, the problems of insufficient detection precision, high cost, lack of systematic reports and the like in the prior art are solved, and an efficient and accurate technical means is provided for safety management of road intersections.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, and in particular to a sight distance triangle traffic safety hidden danger detection method and system based on point cloud data. BACKGROUND

[0002] In the field of road traffic safety, especially at road intersections, the sight distance triangle is a key concept to ensure driving safety. The triangle area (usually composed of the stopping sight distances of two intersecting lanes) must maintain unobstructed visibility, and no objects or road facilities that obstruct the mutual observation of drivers are allowed. The purpose is to ensure that drivers have enough time and distance to discover potential traffic conflicts (such as lateral vehicles) before entering the intersection and take braking or avoidance measures, thereby effectively preventing collision accidents.

[0003] However, the accurate detection and evaluation of obstacles within the intersection sight distance triangle have long faced significant challenges. The three-dimensional properties of obstacles, especially height, and the diversity of types (such as green vegetation, temporary facilities, billboards, buildings, etc.) complicate the detection work. Existing technologies mainly rely on the following three methods, but all have obvious limitations:

[0004] 1. Manual patrol judgment: relying on human eyes to investigate and judge whether there is obstruction on site. This method is inefficient, highly subjective, difficult to quantify the degree of obstruction, and greatly affected by environment, weather, and personnel experience, making it impossible to achieve large-scale, standardized detection.

[0005] 2. Image recognition based on monitoring video: installing monitoring cameras at intersections and using computer vision technology to analyze the screen to identify obstacles. This method requires additional deployment and maintenance of expensive monitoring equipment, which is costly. More importantly, two-dimensional image-based recognition is difficult to accurately determine the true height of the object (especially whether it exceeds the key safety threshold of 1.2 meters) and its actual range of obstruction to the driver's line of sight in three-dimensional space, and the detection angle is single and easily disturbed by factors such as light, angle, and texture of the obstruction itself.

[0006] 3. Sensor detection (such as infrared): using specific sensors (such as infrared sensors) to detect objects within the sight distance triangle area. Similarly, it requires the installation of special equipment, increasing costs. Its main disadvantage is that it can usually only detect the presence and approximate location of obstacles, making it difficult to accurately measure their height, quantify their specific obstruction distance and range to the driver's line of sight, and limited in recognizing complex-shaped obstacles (such as dense foliage).

[0007] The existing technologies generally have the following key deficiencies:

[0008] High detection accuracy is insufficient: it is difficult to reliably and accurately identify obstacles that exceed 1.2 meters in height, which is an important threshold affecting the line of sight of car drivers, and actually cause visual obstruction.

[0009] Dependence on additional hardware, high cost: both monitoring identification and sensor detection require deployment and maintenance of additional special equipment, which increases application cost.

[0010] Obstruction quantification and visualization are difficult: existing methods cannot accurately measure and visualize the specific obstruction distance and spatial range of the line of sight within the visual triangle, and can only provide approximate location information of the obstacle, resulting in detection results lacking specific and operable guidance significance for subsequent hidden danger rectification.

[0011] Lack of systematic analysis and reporting: unable to automatically generate a systematic analysis report containing detailed obstacle information, location, impact range and targeted rectification suggestions.

[0012] In summary, the existing intersection visual triangle obstacle detection technology cannot meet the actual needs of high efficiency, precision, low cost and quantification, especially in height judgment, three-dimensional obstruction range quantification and result application guidance. This limits the ability of traffic management departments to effectively identify and manage such safety hazards. Therefore, there is an urgent need for a more intelligent, efficient and accurate three-dimensional data-based technology to automatically generate a visual triangle, accurately detect and quantify obstacles above the safety threshold within it, and provide intuitive visualization results and specific rectification suggestions to improve the scientificity and effectiveness of intersection safety management. SUMMARY

[0013] To this end, the embodiments of the present application provide a visual triangle traffic safety hazard detection method and system based on point cloud data, which is used to solve the problems of existing technology that the intersection visual obstruction is difficult to quantify and visualize, the obstacle detection (especially the obstacle with a height exceeding 1.2 meters) is single in angle and insufficient in accuracy, the dependence on additional hardware is high in cost, and the system analysis report and rectification suggestion with guiding significance cannot be generated.

[0014] To solve the above problems, the embodiments of the present application provide a visual triangle traffic safety hazard detection method based on point cloud data, which comprises:

[0015] S1: collecting point cloud data of a road intersection and converting it into a standard three-dimensional format, while obtaining road speed limit data;

[0016] S2: preprocessing the point cloud data to generate a vector road network, and associating the road speed limit data as a road network attribute;

[0017] S3: automatically generate the sight distance triangle range of the corresponding lane speed according to the road speed limit data in the road network attribute;

[0018] S4: lift the sight distance triangle plane to a set height from the ground, scan the point cloud data in the height plane through ray collision detection, and identify the obstacles and their spatial positions with a height greater than or equal to the set height;

[0019] S5: generate a pre-warning information list containing the positions and types of the obstacles according to the identification results, and support double-mode visual backtracking of high-altitude view and driver's view;

[0020] S6: output a standardized hidden danger identification report containing an obstacle positioning map and rectification suggestions.

[0021] Preferably, the step S1 comprises:

[0022] The point cloud data in a set length range of the road intersection is collected by a handheld or vehicle-mounted device, converted into las format, further converted into 3DTiles format, and the shrubs, signboards and buildings in the intersection area are individually processed.

[0023] Preferably, the step S2 comprises:

[0024] The part above the ground in the point cloud data is cut, orthographic image TIF data is generated and converted into ITIF format; the road vectorization is performed based on the ITIF data to obtain the road network structure data, and the road speed limit data is recorded as the attribute data of the lane.

[0025] Preferably, the generation logic of the sight distance triangle side length in the step S3 is:

[0026] The side length is dynamically determined according to a preset mapping relationship table of the lane design speed and the safe stopping sight distance.

[0027] Preferably, the ray collision detection in the step S4 is specifically implemented as:

[0028] A certain point in the sight distance triangle plane lifted to a set height from the ground is taken as the origin, dense ray beams are emitted to the surroundings, the coordinate point first colliding with the point cloud is captured, and when the height of the collision point is greater than or equal to the set height, it is determined as an effective obstacle, otherwise it is an ineffective obstacle.

[0029] Preferably, the step S5 comprises:

[0030] The type of the obstacle is automatically labeled, and the double blocking effect of the obstacle on the high-altitude overlooking view and the driver's eye view when the vehicle drives in the selected lane is dynamically simulated in the three-dimensional scene.

[0031] Preferably, the report generation in the step S6 comprises:

[0032] Automatically embed the intersection geographical location overview map with barrier markers, and support manual supplement of latitude and longitude coordinates, road segment names and customized rectification suggestions.

[0033] The embodiment of the application further provides a line-of-sight triangle traffic safety hidden danger detection system based on point cloud data, which is used for realizing the line-of-sight triangle traffic safety hidden danger detection method based on point cloud data.

[0034] A data acquisition module is configured to acquire point cloud data of a road intersection and convert the point cloud data into a standard three-dimensional format, and acquire road speed limit data;

[0035] A road network construction module is configured to preprocess the point cloud data to generate a vector road network, and associate the road speed limit data as road network attributes;

[0036] A line-of-sight calculation module is configured to automatically generate a line-of-sight triangle range corresponding to a lane speed according to the road speed limit data in the road network attributes;

[0037] An obstacle detection module is configured to lift a line-of-sight triangle plane to a set height from the ground, scan point cloud data in the height plane through ray collision detection, and identify obstacles and spatial positions of the obstacles with a height greater than or equal to the set height;

[0038] A visualization platform is configured to generate a warning information list containing obstacle positions and types according to the identification results, and support high-altitude visual angle and driver visual angle dual-mode visual backtracking;

[0039] A report generator is configured to output a standardized hidden danger identification report containing an obstacle positioning map and rectification suggestions.

[0040] The embodiment of the application further provides an electronic device, which comprises a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored in the memory to realize the line-of-sight triangle traffic safety hidden danger detection method based on point cloud data.

[0041] The embodiment of the application further provides a computer storage medium, which stores a computer software product, the computer software product comprises a plurality of instructions, and is used for enabling a computer device to execute the line-of-sight triangle traffic safety hidden danger detection method based on point cloud data.

[0042] As can be seen from the above technical solutions, the present application has the following beneficial effects:

[0043] (1) Three-dimensional quantitative detection improves precision and efficiency, and reduces hardware cost: the application replaces the single angle detection of traditional two-dimensional image recognition or infrared sensor through point cloud data acquisition and three-dimensional modeling, accurately quantifies the spatial position and height of obstacles above 1.2 meters, and solves the problem that the shielding range is difficult to quantify in the prior art. At the same time, without additional deployment of monitoring equipment or sensors, data acquisition can be completed only by handheld / vehicle-mounted point cloud equipment, reducing hardware investment cost; automatic generation of sight distance triangle and ray collision detection replaces manual inspection, improves detection efficiency, and realizes the change from "manual subjective judgment" to "three-dimensional accurate quantification".

[0044] (2) Dual-view visualization and standardized report form a governance closed loop: the application dynamically simulates the shielding effect of obstacles on the line of sight through high-altitude view and driver view double-mode backtracking, which realizes the three-dimensional presentation of shielding effect compared with the defects of the prior art which only provides approximate range; the system automatically generates a standardized report containing obstacle positioning map and rectification suggestions, supports manual supplement of geographic location and customized suggestions, and forms a whole-process closed loop of "detection-warning-rectification". For example, the report clearly marks the specific scheme of shrub pruning height, signboard migration coordinates, etc., which improves the rectification efficiency and provides scientific and feasible decision basis for traffic management. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed in the embodiments will be briefly described below. The features and advantages of the application can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as any limitation on the application. Those skilled in the art can obtain other drawings according to these drawings without creative labor. Among them:

[0046] Figure 1 A flow chart of a sight distance triangle traffic safety hidden danger detection method based on point cloud data provided by the application;

[0047] Figure 2 A sight distance triangle schematic diagram in the application;

[0048] Figure 3 A block diagram of a sight distance triangle traffic safety hidden danger detection system based on point cloud data provided by the application. DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0050] Embodiment one

[0051] In order to solve the problems in the prior art that the intersection sight distance obstruction is difficult to quantify and visualize, the obstacle detection (especially the obstacle with a height exceeding 1.2 meters) has a single angle and insufficient precision, relies on additional hardware with high cost, and cannot generate a system analysis report and rectification suggestion with guiding significance, as shown in Figure 1 The present application proposes a sight distance triangle traffic safety hidden danger detection method based on point cloud data, which comprises the following steps:

[0052] S1: collecting point cloud data of a road intersection and converting the point cloud data into a standard three-dimensional format, and acquiring road speed limit data;

[0053] S2: preprocessing the point cloud data to generate a vector road network, and associating the road speed limit data as a road network attribute;

[0054] S3: automatically generating a sight distance triangle range corresponding to a lane speed according to the road speed limit data in the road network attribute;

[0055] S4: lifting the sight distance triangle plane to a set height from the ground, scanning the point cloud data in the elevation plane through ray collision detection, and identifying obstacles with a height greater than or equal to the set height and their spatial positions;

[0056] S5: generating a pre-warning information list containing the positions and types of the obstacles according to the identification results, and supporting double-mode visual backtracking of a high-altitude view and a driver's view;

[0057] S6: outputting a standardized hidden danger identification report containing an obstacle positioning map and rectification suggestions.

[0058] From the above technical scheme, the application provides a sight triangle traffic safety hidden danger detection method based on point cloud data, which acquires point cloud data of an intersection and converts it into a standard three-dimensional format, breaks away from the hardware cost limitations of traditional two-dimensional image or sensor dependence, and realizes accurate acquisition of three-dimensional spatial information; the preprocessed point cloud generates a vector road network with associated speed limit attributes, provides data support for automatic generation of sight triangles, and ensures deep integration of road network and speed limit information; the sight triangle is automatically generated according to the speed limit, replaces manual subjective setting, and realizes dynamic standardized matching of the detection range; the triangle plane is lifted to 1.2 meters and the obstacles are identified through ray collision detection, the height of the shielding object higher than 1.2 meters is accurately positioned, and the problem of insufficient height detection accuracy in the prior art is solved; an early warning list containing the position and type of the obstacle is generated, and double-view visual backtracking is supported, the line-of-sight shielding effect is quantified from the high-altitude overhead view and the driver's eye view, and the evaluation comprehensiveness is improved; a standardized report with positioning map and rectification suggestion is output, a "detection-analysis-governance" closed loop is formed, and the defect that the report of the prior art lacks practical operation guidance is solved. Through point cloud three-dimensional modeling, automatic sight calculation, accurate collision detection and multi-dimensional visualization, the quantitative analysis and scientific governance of the sight shielding hidden danger are realized, and full-process intelligent support from data acquisition to rectification decision is provided for traffic management.

[0059] In step S1, point cloud data of a road intersection is collected and converted into a standard three-dimensional format, and road speed limit data is acquired, specifically including:

[0060] Collection content and range determination: determine the content of the point cloud data to be collected, including the point cloud data within the range of 100m along the road intersection in each direction with the intersection as the center, and acquire the road speed limit data, which can be obtained by judging the road type and road speed limit sign.

[0061] Collection equipment and method: handheld point cloud instruments or vehicle-mounted point cloud scanning instruments are used for data collection. For example, handheld collection equipment is used to measure the intersection data on site to ensure that the collected data covers the intersection area and the surrounding area.

[0062] Data format conversion and processing: the collected point cloud data is first exported in las format, and then converted into 3DTiles format using CesiumLab software. The single-body processing is performed on the shrubs, sign poles, buildings and the like in the intersection area for subsequent accurate detection.

[0063] In step S2, the point cloud data is preprocessed to generate a vector road network, and the road speed limit data is associated as a road network attribute, specifically including:

[0064] Point cloud data cutting and image generation: import the collected point cloud data into microstationv8i software, cut off the part higher than the ground points, and output into orthophoto TIF format.

[0065] Image format conversion: convert the TIF format orthophoto to ITIF format to prepare for subsequent vectorization processing.

[0066] Road network vectorization: taking the orthophoto data in ITIF format as the base map, the road network is vectorized in the map and management software to generate road network structure data. The road speed limit data is recorded as the attribute data of the lane to realize the association of speed limit data and road network.

[0067] In step S3, according to the road speed limit data in the road network attribute, the sight triangle range corresponding to the lane speed is automatically generated, which specifically includes:

[0068] Data import and intersection information setting: new intersection information is set in the system, including intersection naming and other basic information, and the point cloud data and road network data after format conversion are imported into the system. The point cloud data and road network data are published in the form of online service to facilitate viewing from different perspectives in the system interface.

[0069] Sight triangle generation logic: point the two intersecting lanes at the intersection, and the system automatically generates a sight triangle according to the preset mapping relationship table between the lane design speed and the safe stopping sight distance (as shown in Table 1). Figure 2

[0070] The length of the sight triangle is determined by the speed limit of the corresponding lane, for example: the sight triangle length of the speed limit 40km / h lane is 40m; the sight triangle length of the speed limit 30km / h lane is 30m.

[0071] Table 1: Mapping relationship table between lane design speed and safe stopping sight distance

[0072]

[0073] In step S4, the sight triangle plane is lifted to a set height from the ground, and the point cloud data in the elevation plane is scanned through ray collision detection to identify obstacles with a height greater than or equal to the set height and their spatial positions, which specifically includes:

[0074] Sight triangle elevation setting: the generated sight triangle is translated to a vertical distance of 1.2m from the ground to obtain a triangle plane with a height of 1.2m from the ground. This height is a key threshold that affects the driver's line of sight.

[0075] ​Ray collision detection principle and implementation: Take a point in the 1.2m high triangle plane as the origin, and send rays in all directions. When the rays collide with the model added with the collider (objects in point cloud data), stop emitting and return the first intersection point with the ray in the scene. Collision detection logic: If the height of the collision point is greater than or equal to 1.2m, it is determined as an effective obstacle; otherwise, it is an invalid obstacle. By scanning the point cloud data in the line-of-sight triangle, obstacles and their spatial positions are identified.

[0076] In step S5, a pre-warning information list containing obstacle positions and types is generated according to the identification results, supporting dual-mode visual backtracking of high-altitude view and driver view, including:

[0077] Pre-warning information generation and type labeling: According to the collision detection results, the type of intersecting point cloud data is determined, and a pre-warning information list is generated to show the existing obstacles and determine their types (such as shrubs, signs, buildings, etc.).

[0078] Dual-view backtracking function: Support backtracking in the pre-warning information list layer:

[0079] High-altitude view: Simulate the vehicle driving on the selected lane from a high-altitude perspective, and intuitively show the overall distribution of obstacles within the line-of-sight triangle and the range of influence on the line of sight.

[0080] Driver's view: Simulate the driver's field of view, dynamically display the real-time occlusion of obstacles to the line of sight during vehicle driving, and facilitate intuitive assessment of the occlusion degree.

[0081] In step S6, a standardized hidden danger identification report is output, including obstacle positioning map and rectification suggestions, including:

[0082] Automatic report generation: The system automatically generates a hidden danger identification report in a standardized format, including basic information such as the location, type, and impact analysis of obstacles on the line of sight.

[0083] Manual supplement and optimization: After exporting the report, the report can be manually modified and supplemented with the following content:

[0084] Overview of the geographical location of the hidden danger intersection, marking the specific location of the obstacles.

[0085] Supplement detailed geographical location information such as the latitude and longitude coordinates of the intersection, road segment name, etc.

[0086] According to the actual road conditions, add targeted rectification suggestions such as pruning shrubs, moving signs, etc.

[0087] Format modification: The report format is modified to ensure clear content structure, accurate data, and standardized charts, enhancing the practicality and operability of the report and providing scientific decision-making basis for hidden danger management.

[0088] Through the above specific embodiments, the present application realizes the automatic detection of traffic safety hidden dangers based on point cloud data and solves the problems of insufficient detection accuracy, high cost, and lack of systematic report in the prior art, providing an efficient and accurate technical means for road intersection safety management.

[0089] Embodiment two

[0090] As shown in Figure 3 The present application provides a traffic safety hidden danger detection system based on point cloud data and line-of-sight triangle, which is used to realize the traffic safety hidden danger detection method based on point cloud data and line-of-sight triangle of the above embodiment one, and specifically includes:

[0091] The data acquisition module 100 is used to acquire point cloud data of the road intersection and convert it into a standard three-dimensional format, and to obtain road speed limit data;

[0092] The road network construction module 200 is used to preprocess the point cloud data to generate a vector road network, and to associate the road speed limit data as road network attributes;

[0093] The line-of-sight calculation module 300 is used to automatically generate a line-of-sight triangle range corresponding to the lane speed according to the road speed limit data in the road network attributes;

[0094] The obstacle detection module 400 is used to lift the line-of-sight triangle plane to a set height above the ground, and to scan the point cloud data in the high elevation plane through ray collision detection to identify obstacles with a height greater than or equal to the set height and their spatial positions;

[0095] The visualization platform 500 is used to generate a warning information list containing the positions and types of obstacles according to the identification results, and to support dual-mode visualization backtracking of high-altitude and driver perspectives;

[0096] The report generator 600 is used to output a standardized hidden danger identification report containing obstacle positioning maps and rectification suggestions.

[0097] The point cloud data based visual range triangle traffic safety hidden danger detection system of the embodiment is used for realizing the point cloud data based visual range triangle traffic safety hidden danger detection method, and therefore the specific embodiments of the point cloud data based visual range triangle traffic safety hidden danger detection system can refer to the embodiment part of the point cloud data based visual range triangle traffic safety hidden danger detection method, for example, the data acquisition module 100, the road network construction module 200, the visual range calculation module 300, the obstacle detection module 400, the visualization platform 500, and the report generator 600 are respectively used for realizing steps S1, S2, S3, S4, S5, and S6 in the point cloud data based visual range triangle traffic safety hidden danger detection method, and therefore the specific embodiments can refer to the description of the respective embodiment part, and details are not described herein again to avoid redundancy.

[0098] Embodiment three

[0099] The embodiment of the application provides an electronic device, which comprises a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored in the memory to realize the point cloud data based visual range triangle traffic safety hidden danger detection method.

[0100] Embodiment four

[0101] The embodiment of the application provides a computer storage medium, which stores a computer software product, the computer software product comprises a plurality of instructions, and is used for enabling a computer device to execute the point cloud data based visual range triangle traffic safety hidden danger detection method.

[0102] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product in the form of one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0103] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0104] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks

[0105] Obviously, the above-mentioned embodiments are only examples for clearly illustrating the present application and are not intended to limit the present application. Further, on the basis of the above-mentioned embodiments, other different forms of changes or variations can be made by those skilled in the art. Here, it is not necessary or possible to enumerate all the embodiments. The obvious changes or variations derived therefrom are still within the scope of the present application.

Claims

1. A method for detecting traffic safety hazards based on point cloud data and a line-of-sight triangle, characterized in that, The method comprises the following steps: S1: collecting point cloud data of a road intersection and converting it into a standard three-dimensional format, while obtaining road speed limit data; S2: preprocessing the point cloud data to generate a vector road network, and associating the road speed limit data as road network attributes; S3: automatically generating a sight triangle range corresponding to the lane speed according to the road speed limit data in the road network attributes; S4: lifting the sight triangle plane to a set height above the ground, scanning the point cloud data in the elevation plane through ray collision detection, and identifying obstacles with a height greater than or equal to the set height and their spatial positions; S5: generating a pre-warning information list containing the positions and types of obstacles according to the identification results, and supporting dual-mode visual backtracking of high-altitude and driver perspectives; S6: outputting a standardized hidden danger identification report containing an obstacle positioning map and rectification suggestions.

2. The point cloud data based visual range triangle traffic safety hazard detection method according to claim 1, characterized in that, The step S1 comprises: Collecting point cloud data within a set length range of the road intersection by a handheld or vehicle-mounted device, converting it into a las format, further converting it into a 3DTiles format, and performing single-body processing on shrubs, signboards, and buildings in the intersection area. 3.The line-of-sight triangle traffic safety hazard detection method based on point cloud data according to claim 1, wherein, The step S2 comprises: Cutting the part of the point cloud data above the ground to generate a TIF orthographic image and convert it into an ITIF format; performing road vectorization based on the ITIF data to obtain road network structure data, and recording the road speed limit data as attribute data of the lane.

4. The LoD triangle based point cloud data traffic safety hazard detection method of claim 1, wherein, The generation logic of the side length of the sight triangle in the step S3 is: Dynamically determining the side length according to a preset mapping relationship table of the lane design speed and the safe stopping sight distance.

5. The point cloud data based line of sight triangle traffic safety hazard detection method of claim 1, wherein, The ray collision detection in the step S4 is specifically implemented as: Taking a point in the sight triangle plane lifted to a set height above the ground as the origin, emitting dense ray beams in all directions, capturing the coordinate point of the first collision with the point cloud, and determining it as an effective obstacle when the collision point height is greater than or equal to the set height, otherwise it is an invalid obstacle.

6. The point cloud data based line of sight triangle traffic safety hazard detection method of claim 1, wherein, The step S5 comprises: Automatically labeling the type of the obstacle, and dynamically simulating the double shielding effect of the obstacle on the high-altitude overhead view and the driver's eye view when a vehicle is driving in the selected lane in the three-dimensional scene.

7. The point cloud data based line of sight triangle traffic safety hazard detection method of claim 1, wherein, The report generation in the step S6 comprises: Automatically embedding an intersection geographical location overview map with obstacle markers, and supporting manual supplement of latitude and longitude coordinates, road segment names, and customized rectification suggestions. 8.A system for detecting traffic safety hazards based on a line-of-sight triangle of point cloud data, the system comprising: The system is used to implement the sight triangle traffic safety hidden danger detection method based on point cloud data according to any one of claims 1 to 7, and specifically comprises: A data acquisition module for collecting point cloud data of a road intersection and converting it into a standard three-dimensional format, while obtaining road speed limit data; A road network construction module for preprocessing the point cloud data to generate a vector road network, and associating the road speed limit data as road network attributes; A sight calculation module for automatically generating a sight triangle range corresponding to the lane speed according to the road speed limit data in the road network attributes; An obstacle detection module for lifting the sight triangle plane to a set height above the ground, scanning the point cloud data in the elevation plane through ray collision detection, and identifying obstacles with a height greater than or equal to the set height and their spatial positions; A visualization platform is configured to generate a pre-warning information list containing obstacle positions and types according to the identification result, and support dual-mode visualization backtracking of high-altitude view and driver view. A report generator is configured to output a standardized hazard identification report containing an obstacle positioning map and rectification suggestions.

9. An electronic device, comprising: The electronic device comprises a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored in the memory to realize the line-of-sight triangle traffic safety hazard detection method based on point cloud data in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer software product, the computer software product comprises a plurality of instructions, and is used to make a computer device execute the line-of-sight triangle traffic safety hazard detection method based on point cloud data in any one of claims 1 to 7.

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