Simulation evaluation method and system for deployment effect of intelligent network connection roadside infrastructure

Abstract

The invention relates to the technical field of intelligent network connection automobiles, and discloses an intelligent network connection roadside infrastructure deployment effect simulation evaluation method and system, and the method comprises the following steps: S1, obtaining a distance error function of a sensor in a target analysis region; s2, carrying out pixelation processing on an intersection map corresponding to the target analysis area; s3, generating perception precision effect distribution according to the intersection form and the sensor deployment scheme; and S4, calculating an intersection comprehensive perception ability evaluation index and a road data service level comprehensive evaluation index. According to the method, the deployment effect of the intelligent network connection roadside infrastructure can be visually and quantitatively evaluated, and the standardization, scientificity, rationality and economical efficiency of intelligent network connection road side infrastructure construction can be guaranteed.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicle technology, specifically to a simulation evaluation method and system for the deployment effect of intelligent connected roadside infrastructure. Background Technology

[0002] The vehicle-road-cloud integrated system refers to a cyber-physical system that integrates the physical and information spaces of people, vehicles, roads, and the cloud through next-generation information and communication technologies. Based on collaborative perception, decision-making, and control, it achieves safe, efficient, energy-saving, and comfortable operation of intelligent connected vehicles and transportation systems. Since 2019, building upon vehicle-road cooperation, cloud computing and other supporting platforms have been introduced to achieve safer and more reliable automated driving by utilizing information from platforms such as maps and weather. This is crucial for the industrialization of the "China solution" for intelligent connected vehicles and is also an important solution for smart transportation and smart cities.

[0003] During the initial construction of vehicle-to-everything (V2X) demonstration zones and pilot zones, as well as subsequent project acceptance processes, it was discovered that some roadside infrastructure renovation plans were not designed in accordance with actual road conditions and subsequent service operation needs. Only in later use were issues such as low accuracy and blind spots found, making it impossible to support actual operation. This has resulted in inconsistent roadside infrastructure construction standards across different regions, difficulty in guaranteeing service quality, and a lack of trust in roadside data among automakers. Summary of the Invention

[0004] The present invention aims to provide a simulation evaluation method and system for the deployment effect of intelligent connected roadside infrastructure, which can intuitively and quantitatively evaluate the deployment effect of intelligent connected roadside infrastructure, and help ensure the standardization, scientificity, rationality and economy of the construction of intelligent connected intersection facilities.

[0005] To achieve the above objectives, the present invention provides the following basic solution.

[0006] Option 1 The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure includes the following steps: S1, Obtain the distance error function of the sensor in the target analysis area; S2, pixelate the intersection map corresponding to the target analysis area; S3 generates the distribution of perception accuracy based on the intersection shape and sensor deployment scheme; S4 is the comprehensive evaluation index for intersection perception capability and the comprehensive evaluation index for road data service level.

[0007] Furthermore, in S1, the distance error function is: ; In solving At the same time, the position information of multiple sensors is sampled first. With RTK location information : ; Then obtain the positional error for each set of data. ; The distance error function is obtained by performing a second-order fit between d and E. : In the formula, d is the absolute distance from the sensor; E is the distance error between the sensor position and the RTK position.

[0008] Furthermore, in S2, the pixelation process includes the following sub-steps: S2.1, denote the bottom left corner of the map as the origin. Take the original intersection map data Subtract the origin and multiply by the scaling factor required for the desired precision to obtain the rasterized map data: ;in, This refers to the scaling factor; S2.2, Generate a two-dimensional array ,in , ; S2.3, take any Calculate the sum of the radians of the angle between any two adjacent vertices of the map outline: ; ; ; in ; Determine whether the point is inside the intersection, and assign a value based on the following conditions: ; S2.4, repeat S2.3 until all processes are completed. Combination; thus, Includes pixelated intersection data; S2.5, filter out intersections The position is determined, and S2.3 is repeated to further determine whether the point is within an obstacle, while assigning values ​​according to the following conditions: ; in, This is an invalid region; This refers to the intersection area; It is an intersection area that is obscured by obstacles.

[0009] Furthermore, S3 includes the following sub-steps: S3.1, optional sensor Simultaneously generate and arrays of the same size Used to store position errors, where ; S3.2, take any Calculate its relationship with the sensor Detection distance Angle with the sensor's line of sight At the same time, the maximum detection distance of the sensor is obtained according to the sensor manual. Maximum detection angle Then, based on the following conditions... Assign a value: ; S3.3, repeat S3.2, until all processes are completed. Combination; thus, It contains pixelated intersection data.

[0010] S3.4, repeat S3.1~S3.3 until all sensors have been processed and the accuracy distribution of each sensor is obtained: ; S3.5, take Each component The minimum value is the distribution of perceived accuracy effect. ;in This is the pixelated position information.

[0011] Furthermore, the target analysis area is the intersection area.

[0012] Furthermore, in S4, the calculation of the comprehensive perception capability evaluation index for intersections includes the following calculation steps: Based on the distribution of perception accuracy effects Based on the intersection's shape, center, stop line, and 200m beyond the stop line, the intersection area is divided into a center area, an entrance area, an exit area, a pedestrian crossing area, and a road channelization facility area; Divided into the central area of ​​the intersection Entering the driving direction area Export areas Pedestrian crossing area Channelized areas , ; Calculate the comprehensive perception capability evaluation index of the intersection using the following formula: ; In the formula, For the evaluation index of comprehensive perception capability at intersections; , , , , These correspond to the central area of ​​the intersection. Entering the driving direction area Export areas Pedestrian crossing area Channelized areas The weighted value, and .

[0013] Furthermore, in S4, the calculation of the comprehensive evaluation index for road data service level includes the following calculation steps: Based on the distribution of perception accuracy effects Based on service capacity requirements, relying on the center of intersections and stop lines, from Select the pixel index number corresponding to each service capability. The number of pixels approximates the area range. ; The effective accuracy coverage is calculated using the following formula: ; Let the precision distribution of RSU be: The effective broadcast signal coverage rate is: ; The comprehensive service level evaluation index is calculated using the following formula: ; in, The weighted values ​​are those of the indicator, and .

[0014] Option 2 A simulation and evaluation system for the deployment effect of intelligent connected roadside infrastructure includes computer equipment that is programmed or configured to perform the following steps: Step 1: Generate a visualization solution layer based on equipment selection and configuration and sensor deployment plan; Step 2: Adjust the deployment and installation location and model of the sensor; Step 3: Analyze or add road environment elements based on map data; Step 4: Based on the current sensor deployment scheme, generate a simulation perception accuracy distribution layer, an effective communication strength coverage layer, and a sensor coverage visualization simulation layer; at the same time, according to the simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure as described in Scheme 1, generate the comprehensive perception capability evaluation index and the comprehensive evaluation index for road data service level corresponding to the current scheme.

[0015] The working principle and advantages of this invention are as follows: Regarding Option 1: This solution provides a direct and quantitative evaluation of the deployment effectiveness of intelligent connected roadside infrastructure, enabling a scientific assessment of the infrastructure through a systematic process. Specifically, it first quantifies the attenuation characteristics of the hardware's sensing accuracy by acquiring the distance error function of the sensors. This allows for precise quantification of sensor performance, ensuring that subsequent simulation evaluations are based on accurate fundamental data. Secondly, the intersection map is pixelated, transforming complex geospatial information into a computer-processable pixel format for easier computation. Furthermore, pixelation captures the spatial features of the intersection more precisely, allowing for more accurate determination of sensor coverage and blind spots during sensing accuracy analysis, and facilitating quantification. Finally, the solution combines intersection morphology and sensor deployment schemes to generate a sensing accuracy distribution. Different intersection morphologies affect the sensor's sensing range and effectiveness. Analyzing sensing accuracy based on intersection morphology and sensor deployment schemes provides a clear view of the sensing accuracy in different areas, helping to identify problems in the deployment scheme. Furthermore, this plan also establishes an evaluation index for comprehensive perception capabilities at intersections and a comprehensive evaluation index for road data service levels, which can further comprehensively evaluate the deployment effect of intelligent connected roadside infrastructure from different dimensions.

[0016] Regarding Option Two: To address the issue of the inability to intuitively and in real-time obtain the perception effect of the post-construction sensor deployment scheme when designing and constructing intelligent connected roads, this solution provides equipment selection accuracy analysis, road perception capability level assessment, and data service planning accuracy evaluation, ultimately presenting the fused perception effect simulation. This ensures the standardization, scientific rigor, rationality, and economy of intelligent connected intersection facility construction. Key features include: First, this solution can generate a perception accuracy distribution layer based on intersection morphology, achieving perception accuracy visualization and helping to intuitively identify perception blind spots and weak points of the current deployment scheme affected by the environment. Second, this solution can obtain a comprehensive perception accuracy evaluation index for intersections by comprehensively weighting the accuracy of intersection zones, enabling quantitative assessment of the intersection perception accuracy level and accurately evaluating the corresponding roadside perception capability level. Third, based on operational service needs, this solution can provide comprehensive evaluation and verification of different data service levels (corresponding to comprehensive evaluation indicators of road data service levels) for signal control data services, perception target data services (cooperative driving), perception target data services (autonomous driving), traffic situation data services, and traffic event data services, making the post-construction service capabilities of intelligent connected roads verifiable. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method of the simulation evaluation method and system for the deployment effect of intelligent connected roadside infrastructure of the present invention, in Implementation Example 1. Figure 2This is a schematic diagram of the system operation process of Embodiment 2 of the Simulation Evaluation Method and System for Deployment Effect of Intelligent Connected Roadside Infrastructure of the present invention. Detailed Implementation

[0018] The following detailed explanation illustrates the specific implementation methods: Example 1 The basic implementation examples are as follows: Figure 1 The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure, as shown, includes the following steps: S1, Obtain the distance error function of the sensor in the target analysis area; The distance error function is: ; In solving At the same time, the position information of multiple sensors is sampled first. With RTK location information In this embodiment, with the device as the origin, 40 sets of sensor position information and RTK position information are sampled sequentially at 5m intervals: ; Then obtain the positional error for each set of data. ; The distance error function is obtained by performing a second-order fit between d and E. : ; In the formula, d represents the absolute distance to the sensor; E represents the distance error between the sensor position and the RTK position. The sensors include different types of roadside infrastructure equipment such as lidar, millimeter-wave radar, and cameras.

[0019] S2, the intersection map corresponding to the target analysis area is pixelated. The target analysis area is the intersection area.

[0020] The pixelation process includes the following sub-steps: S2.1, denote the bottom left corner of the map as the origin. Take the original intersection map data Subtract the origin and multiply by the scaling factor required for the desired precision to obtain the rasterized map data: ;in, This refers to the scaling factor. For example, when the map unit is meters (m) and the error unit is centimeters (cm), the corresponding scaling factor is 100.

[0021] S2.2, Generate a two-dimensional array ,in , .

[0022] S2.3, take any Calculate the sum of the radians of the angle between any two adjacent vertices of the map outline: ; ; ; in ; Determine whether the point is inside the intersection, and assign a value based on the following conditions: ; S2.4, repeat S2.3 until all processes are completed. Combination; thus, Includes pixelated intersection data; S2.5, filter out intersections The position is determined, and S2.3 is repeated to further determine whether the point is within an obstacle, while assigning values ​​according to the following conditions: ; in, This is an invalid region; This refers to the intersection area; , represents the intersection area that is obscured by obstacles; i, j are the rasterized point data, i.e., the pixelated location information.

[0023] S3 generates the distribution of perception accuracy based on the intersection shape and sensor deployment scheme.

[0024] Specifically, it includes the following sub-steps: S3.1, optional sensor Simultaneously generate and arrays of the same size Used to store position errors, where ; S3.2, take any Calculate its relationship with the sensor Detection distance Angle with the sensor's line of sight At the same time, the maximum detection distance of the sensor is obtained according to the sensor manual. Maximum detection angle Then, based on the following conditions... Assign a value: ; S3.3, repeat S3.2, until all processes are completed. Combination; thus, It contains pixelated intersection data.

[0025] S3.4, repeat S3.1~S3.3 until all sensors have been processed and the accuracy distribution of each sensor is obtained: ; S3.5, take Each component The minimum value is the distribution of perceived accuracy effect. ;in This is the pixelated position information.

[0026] S4 is the comprehensive evaluation index for intersection perception capability and the comprehensive evaluation index for road data service level.

[0027] When calculating the comprehensive perception capability evaluation index of an intersection, based on the sensor installation and deployment scheme, intersection shape, and the distribution of perception accuracy, the intersection area is divided into the intersection center area, entrance area, exit area, pedestrian crossing area, and road channelization facility area. Each of these areas is further divided into several perception area units according to a standard of 0.1m*0.1m (in practical applications, this standard is set according to the scaling factor set in pixelation processing). Then, based on the coverage ratio and service capacity configuration requirements of the intersection center area, entrance area, exit area, pedestrian crossing area, and road channelization facility area, a reference weight for the accuracy of each area is generated. Finally, the accuracy of the perception area units within each area is comprehensively weighted to calculate the comprehensive perception accuracy evaluation index of the intersection, thereby determining the intersection's perception capability level.

[0028] The intersection morphology specifically refers to structured data on intersection morphological features, including intersection type, intersection edge lines, number of approach directions, number of approach lanes, road elevation change rate, equipment installation pole data (pole position, arm length, angle), and relative equipment installation position (position on the pole, horizontal angle, and pitch angle). The sensor deployment scheme includes equipment technical parameters and environmental influencing factors. The equipment technical parameters include sensor sensing range and accuracy error distribution, MEC computing power performance, RSU effective communication range and signal strength distribution, and equipment power. The environmental influencing factors include sensor accuracy attenuation rate and environmental occlusion attenuation rate.

[0029] Specifically, the calculation steps are as follows: Based on the distribution of perception accuracy effects Based on the intersection's shape, center, stop line, and 200m beyond the stop line, the intersection area is divided into a center area, an entrance area, an exit area, a pedestrian crossing area, and a road channelization facility area; Divided into the central area of ​​the intersection Entering the driving direction area Export areas Pedestrian crossing area Channelized areas , ; Calculate the comprehensive perception capability evaluation index of the intersection using the following formula: ; In the formula, For the evaluation index of comprehensive perception capability at intersections; , , , , These correspond to the central area of ​​the intersection. Entering the driving direction area Export areas Pedestrian crossing area Channelized areas The weighted value, and .

[0030] When calculating the comprehensive evaluation index of road data service level, the current intersection area is divided into secondary sections according to road data service capabilities, including traffic control data service, perception target data service (cooperative driving), perception target data service (autonomous driving), traffic situation data service, and traffic event data service. The core focus area, effective accuracy coverage, and effective broadcast signal coverage of each service capability are evaluated to obtain the comprehensive evaluation index of the current intersection road data service level.

[0031] Specifically, the calculation steps are as follows: Based on the distribution of perception accuracy effects Based on service capacity requirements, relying on the center of intersections and stop lines, from Select the pixel index number corresponding to each service capability. (i.e., the pixelated location information); where the number of pixels approximates the region range. ; The effective accuracy coverage is calculated using the following formula: ; Let the precision distribution of RSU be: The effective broadcast signal coverage rate is: ; The comprehensive service level evaluation index is calculated using the following formula: ; in, The weighted values ​​are those of the indicator, and .

[0032] This embodiment provides a simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure, which can intuitively quantify and evaluate the deployment effect of intelligent connected roadside infrastructure, and helps to ensure the standardization, scientificity, rationality and economy of the construction of intelligent connected intersection facilities.

[0033] Example 2 like Figure 2 As shown, the intelligent connected roadside infrastructure deployment effect simulation evaluation system includes computer equipment, which is programmed or configured to perform the following steps: Step 1: Generate a visualization solution layer based on equipment selection and configuration and sensor deployment plan; Step 2: Adjust the deployment and installation location and model of the sensor; Step 3: Analyze or add road environment elements based on map data; for example, green belts, obstructions, channelization facilities, and other environmental elements. Step 4: Generate a simulated sensing accuracy distribution layer, an effective communication strength coverage layer, and a sensor coverage visualization simulation layer based on the current sensor deployment scheme. The layers generated here are used to display the simulated sensing accuracy (corresponding to the sensing accuracy effect distribution calculated in Example 1), the effective communication strength coverage (corresponding to the effective broadcast signal coverage area calculated in Example 1), and the sensor coverage area (corresponding to the sensor device sensing range calculated in Example 1).

[0034] Simultaneously, following the simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure as described in Example 1, the comprehensive perception capability evaluation index and the comprehensive evaluation index for road data service level corresponding to the current scheme are generated. Here, based on the comprehensive perception capability evaluation index and the comprehensive evaluation index for road data service level, the overall accuracy of the intersection can be intuitively observed.

[0035] This embodiment provides a simulation evaluation system for the deployment effect of intelligent connected roadside infrastructure. It can provide multi-dimensional analysis content such as equipment selection accuracy analysis, road perception capability level identification, and data service planning accuracy evaluation, and finally present the fusion perception effect simulation. This helps to ensure the standardization, scientificity, rationality and economy of intelligent connected intersection facility construction.

[0036] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure, characterized in that, Includes the following steps: S1, Obtain the distance error function of the sensor in the target analysis area; S2, pixelate the intersection map corresponding to the target analysis area; S3 generates the distribution of perception accuracy based on the intersection shape and sensor deployment scheme; S4 is the comprehensive evaluation index for intersection perception capability and the comprehensive evaluation index for road data service level.

2. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 1, characterized in that, In S1, the distance error function is: ; In solving At the same time, the position information of multiple sensors is sampled first. With RTK location information : ； Then obtain the positional error for each set of data. ; The distance error function is obtained by performing a second-order fit between d and E. : In the formula, d is the absolute distance from the sensor; E is the distance error between the sensor position and the RTK position.

3. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 1, characterized in that, In S2, the pixelation process includes the following sub-steps: S2.1, denote the bottom left corner of the map as the origin. Take the original intersection map data Subtract the origin and multiply by the scaling factor required for the desired precision to obtain the rasterized map data: ;in, This refers to the scaling factor; S2.2, Generate a two-dimensional array ,in , ; S2.3, take any Calculate the sum of the radians of the angle between any two adjacent vertices of the map outline: ； ； ； in ; Determine whether the point is inside the intersection, and assign a value based on the following conditions: ; S2.4, repeat S2.3 until all processes are completed. Combination; thus, Includes pixelated intersection data; S2.5, filter out intersections The position is determined, and S2.3 is repeated to further determine whether the point is within an obstacle, while assigning values ​​according to the following conditions: ; in, This is an invalid region; This refers to the intersection area; , represents the intersection area that is obscured by obstacles; i, j are the rasterized point data.

4. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 1, characterized in that, S3 includes the following sub-steps: S3.1, optional sensor Simultaneously generate and arrays of the same size Used to store position errors, where ; S3.2, take any Calculate its relationship with the sensor Detection distance Angle with the sensor's line of sight At the same time, the maximum detection distance of the sensor is obtained according to the sensor manual. Maximum detection angle Then, based on the following conditions... Assign a value: ； S3.3, repeat S3.2, until all processes are completed. Combination; thus, It contains pixelated intersection data. S3.4, repeat S3.1~S3.3 until all sensors have been processed and the accuracy distribution of each sensor is obtained: ; S3.5, take Each component The minimum value is the distribution of perceived accuracy effect. ;in This is the pixelated position information.

5. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 1, characterized in that, The target analysis area is the intersection area.

6. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 4, characterized in that, In S4, the calculation of the comprehensive perception capability evaluation index for intersections includes the following calculation steps: Based on the distribution of perception accuracy effects Based on the intersection's shape, center, stop line, and 200m beyond the stop line, the intersection area is divided into a center area, an entrance area, an exit area, a pedestrian crossing area, and a road channelization facility area; Divided into the central area of ​​the intersection Entering the driving direction area Export areas Pedestrian crossing area Channelized areas , ; Calculate the comprehensive perception capability evaluation index of the intersection using the following formula: ； In the formula, For the evaluation index of comprehensive perception capability at intersections; , , , , These correspond to the central area of ​​the intersection. Entering the driving direction area Export areas Pedestrian crossing area Channelized areas The weighted value, and .

7. The simulation evaluation method for the deployment effect of intelligent connected roadside infrastructure according to claim 4, characterized in that, In S4, the calculation of the comprehensive evaluation index of road data service level includes the following calculation steps: Based on the distribution of perception accuracy effects Based on service capacity requirements, relying on the center of intersections and stop lines, from Select the pixel index number corresponding to each service capability. The number of pixels approximates the area range. ; The effective accuracy coverage is calculated using the following formula: ； Let the precision distribution of RSU be: The effective broadcast signal coverage rate is: ； The comprehensive service level evaluation index is calculated using the following formula: ； in, The weighted values ​​are those of the indicator, and .

8. A simulation and evaluation system for the deployment effect of intelligent connected roadside infrastructure, characterized in that, This includes computer equipment that is programmed or configured to perform the following steps: Step 1: Generate a visualization solution layer based on equipment selection and configuration and sensor deployment plan; Step 2: Adjust the deployment and installation location and model of the sensor; Step 3: Analyze or add road environment elements based on map data; Step 4: Generate a simulation perception accuracy distribution layer, an effective communication strength coverage layer, and a sensor coverage visualization simulation layer based on the current sensor deployment scheme; simultaneously, generate the intersection comprehensive perception capability evaluation index and the road data service level comprehensive evaluation index corresponding to the current scheme according to the intelligent connected roadside infrastructure deployment effect simulation evaluation method as described in any one of claims 1-7.

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