Intelligent network connection road side sensing data quality evaluation method and device

Abstract

The invention discloses an intelligent network connection road side sensing data quality evaluation method and equipment. The method comprises the following steps: acquiring an intelligent network connection road side sensing data quality evaluation index set; performing normalization processing on evaluation indexes of the intelligent network connection road side sensing data quality evaluation index set to obtain a normalized index set; and based on the normalized index set, calculating a multi-dimensional road side sensing data quality comprehensive evaluation index, and according to the multi-dimensional road side sensing data quality comprehensive evaluation index, calculating the road side sensing data quality comprehensive evaluation index. And identifying the roadside sensing device with the optimal roadside sensing data quality, the manufacturer with the optimal roadside sensing data quality and / or the road position with the optimal roadside sensing data quality. Compared with single-index evaluation, multiple evaluation indexes are comprehensively considered, and the quality of the roadside sensing data can be more comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-related technologies, and in particular to a method for evaluating the quality of intelligent connected roadside perception data, an electronic device, a storage medium, and a computer program product. Background Art

[0002] The roadside perception data collected and processed by roadside perception devices (such as cameras, radars, etc.) and roadside computing units (RCU) are the data basis for the pilot work of the "vehicle-road-cloud integration" application of intelligent connected vehicles. Roadside computing units are deployed along roads, highways or at the end of the field, and cooperate with roadside perception devices (such as cameras, radars, etc.) to complete the modules, equipment or facilities for the aggregation and processing of perception information.

[0003] The quality of roadside perception data determines the implementation effect and quality of the "vehicle-road-cloud integration" solution of intelligent connected vehicles. However, existing technologies lack methods to evaluate the quality of intelligent connected roadside perception data and cannot identify high-quality roadside perception devices and manufacturers. Summary of the invention

[0004] Based on this, it is necessary to provide a method for evaluating the quality of intelligent connected roadside perception data, an electronic device, a storage medium and a computer program product to address the technical problems that the existing technology lacks a method for evaluating the quality of intelligent connected roadside perception data and cannot identify high-quality roadside perception equipment and manufacturers.

[0005] The present invention provides a method for evaluating quality of intelligent network roadside perception data, comprising:

[0006] Obtaining a set of quality evaluation indicators for intelligent networked roadside perception data, wherein the set of quality evaluation indicators for intelligent networked roadside perception data includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods;

[0007] Normalizing the evaluation indicators of the intelligent connected roadside perception data quality evaluation indicator set to obtain a normalized indicator set, wherein the normalized indicator set includes a normalized evaluation indicator after each evaluation indicator is normalized;

[0008] Based on the normalized indicator set, comprehensive evaluation indicators of roadside perception data quality in multiple dimensions are calculated, and according to the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions, the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality and / or the road location with the best roadside perception data quality are identified.

[0009] Furthermore, the normalization of the intelligent connected roadside perception data quality evaluation index set includes:

[0010] The evaluation indicators in the intelligent connected roadside perception data quality evaluation indicator set are normalized and judged:

[0011] If the evaluation index is a pass rate, the pass rate is used as a normalized evaluation index;

[0012] If the evaluation indicator is not the pass rate, and the evaluation indicator is a positive evaluation indicator, the positive evaluation indicator is normalized to a normalized value between 0 and 1 as the normalized evaluation indicator;

[0013] If the evaluation indicator is not the pass rate, and the evaluation indicator is a negative evaluation indicator, the evaluation indicator is converted into a positive evaluation indicator, and the converted evaluation indicator is normalized to a normalized value between 0 and 1 as a normalized evaluation indicator.

[0014] Furthermore, converting the evaluation index into a positive evaluation index includes: calculating a set threshold value minus the evaluation index to obtain a converted evaluation index.

[0015] Further, the calculating of the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions based on the normalized indicator set, and identifying the roadside perception device with the best roadside perception data quality, the manufacturer with the best roadside perception data quality, and / or the road position with the best roadside perception data quality according to the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions, includes:

[0016] Based on the normalized indicator set, calculating the comprehensive evaluation index of the roadside sensing data quality in the roadside sensing device dimension, and identifying the roadside sensing device with the best roadside sensing data quality;

[0017] Based on the normalized indicator set, calculating the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension, and identifying the manufacturer with the best roadside perception data quality;

[0018] Based on the normalized indicator set, a comprehensive evaluation index of the roadside perception data quality in the road position dimension is calculated, and the road position with the best roadside perception data quality is identified.

[0019] Furthermore, the calculating of the comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension based on the normalized index set and identifying the roadside perception device with the best roadside perception data quality includes:

[0020] Calculate a first roadside perception data quality evaluation index set in the roadside perception device dimension for each roadside perception device of each manufacturer at each road position, wherein each of the first roadside perception data quality evaluation index sets includes, in the normalized index set, an average value of similar normalized evaluation indicators of the roadside perception devices corresponding to the manufacturer corresponding to the road position corresponding to the first roadside perception data quality evaluation index set within a time length T;

[0021] The first comprehensive evaluation index of the quality of roadside perception data of each roadside perception device of each manufacturer at each road position in the roadside perception device dimension within the time length T is calculated as follows:

[0022]

[0023] In the formula, S i,j,d,T The first comprehensive evaluation index of the quality of roadside perception data in the roadside perception device dimension at the road location i, manufacturer j, and device d within the time period T. represents the first roadside perception data quality evaluation index set in the roadside perception device dimension of manufacturer j device d at road position i within time T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator;

[0024] Calculate the device with the best roadside perception data quality for manufacturer j at road position i within time T for:

[0025]

[0026] Where N rcu Indicates the number of roadside sensing devices;

[0027] Calculate the device with the best roadside perception data quality at road position i within time T for:

[0028]

[0029] Where N manuf Indicates the number of manufacturers.

[0030] Calculate the device with the best roadside perception data quality within the time T for:

[0031]

[0032] Where N road Indicates the number of road locations.

[0033] Furthermore, the calculating of the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension based on the normalized index set and identifying the manufacturer with the best roadside perception data quality includes:

[0034] Calculate a second roadside perception data quality evaluation index set for each manufacturer at each road position in the manufacturer dimension, each of the second roadside perception data quality evaluation index sets including an average value of similar normalized evaluation indicators of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within a time length T in the normalized index set;

[0035] The comprehensive evaluation index of the second roadside perception data quality of each manufacturer at each road position in the manufacturer dimension within the duration T is calculated as follows:

[0036]

[0037] In the formula, S i,j,T It represents the comprehensive evaluation index of the second roadside perception data quality of manufacturer j at road position i in the manufacturer dimension within the duration T, represents the second roadside perception data quality evaluation index set of manufacturer j at road position i in the manufacturer dimension within time T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator;

[0038] Calculate the manufacturer with the best roadside perception data quality at road position i within time T for:

[0039]

[0040] The manufacturer with the best roadside perception data quality within the calculation time T for:

[0041]

[0042] Where N road Represents the number of road locations, N manuf Indicates the number of manufacturers.

[0043] Furthermore, based on the normalized indicator set, a comprehensive evaluation index of the roadside perception data quality in the road position dimension is calculated, and the road position with the best roadside perception data quality is identified.

[0044] Calculating a third roadside perception data quality evaluation index set for each road position in the road position dimension, each of the third roadside perception data quality evaluation index sets includes an average value of similar normalized evaluation indicators of the road position corresponding to the third roadside perception data quality evaluation index set within a time length T in the normalized index set;

[0045] The third comprehensive evaluation index of the roadside perception data quality in the road position dimension within the time length T for each road position is calculated as:

[0046]

[0047] In the formula, S i,T represents the third comprehensive evaluation index of roadside perception data quality in the road position dimension within the time length T at the road position i, represents the third roadside perception data quality evaluation index set of road position i in the road position dimension within the time length T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, θ NI,1 Indicates the Nth I The angle between the first indicator and the first indicator;

[0048] Calculate the road position with the best roadside perception data quality within the time period T for:

[0049]

[0050] Where N road Indicates the number of road locations.

[0051] The present invention provides an electronic device, comprising:

[0052] at least one processor; and,

[0053] a memory communicatively connected to at least one of the processors; wherein,

[0054] The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the intelligent connected roadside perception data quality evaluation method as described above.

[0055] The present invention provides a storage medium, which stores computer instructions. When a computer executes the computer instructions, it is used to execute all steps of the intelligent connected roadside perception data quality evaluation method as described above.

[0056] The present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for evaluating the quality of intelligent connected roadside perception data as described above.

[0057] The present invention uses a set of intelligent networked roadside perception data quality evaluation indicators, based on multiple evaluation indicators in the intelligent networked roadside perception data quality evaluation indicator set, to comprehensively evaluate the quality of roadside perception data using multiple dimensions, and identifies the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality, and / or the road location with the best roadside perception data quality. Compared with a single indicator evaluation, the present invention comprehensively considers multiple evaluation indicators and can more comprehensively evaluate the quality of roadside perception data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a workflow diagram of a method for evaluating quality of intelligent connected roadside perception data according to an embodiment of the present invention;

[0059] Figure 2 This is a workflow diagram of a method for evaluating quality of intelligent connected roadside perception data according to another embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the distribution of the first roadside perception data quality comprehensive evaluation index in the equipment dimension of an example of the present invention;

[0061] Figure 4 This is a schematic diagram of the distribution of the comprehensive evaluation index of the second roadside perception data quality in the manufacturer dimension in an example of the present invention;

[0062] Figure 5 This is a schematic diagram of the distribution of the third comprehensive evaluation index of roadside perception data quality in the road position dimension according to an example of the present invention;

[0063] Figure 6 A workflow diagram of a method for evaluating quality of intelligent networked roadside perception data according to the best embodiment of the present invention;

[0064] Figure 7 The figure is a schematic diagram of the hardware structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0065] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. The same components are represented by the same reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0066] like Figure 1The figure shows a workflow diagram of a method for evaluating quality of intelligent connected roadside perception data according to an embodiment of the present invention, including:

[0067] Step S101, obtaining an intelligent network roadside perception data quality evaluation indicator set, wherein the intelligent network roadside perception data quality evaluation indicator set includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods;

[0068] Step S102, normalizing the evaluation indicators of the intelligent connected roadside perception data quality evaluation indicator set to obtain a normalized indicator set, wherein the normalized indicator set includes a normalized evaluation indicator after each evaluation indicator is normalized;

[0069] Step S103, based on the normalized indicator set, calculate the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions, and identify the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality and / or the road location with the best roadside perception data quality according to the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions.

[0070] Specifically, the present invention can be applied to electronic devices with processing capabilities, such as cloud servers.

[0071] First, execute step S101 to obtain a set of quality evaluation indicators for intelligent connected roadside perception data, wherein the set of quality evaluation indicators for intelligent connected roadside perception data includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods.

[0072] Among them, roadside perception data is structured perception data formed by roadside edge computing using sensors such as cameras, millimeter-wave radars, and lidar.

[0073] Specifically, the intelligent network roadside perception data quality evaluation index set includes multiple evaluation indicators. The evaluation indicators include but are not limited to:

[0074] Track survival cycle qualification rate, speed qualification rate, etc., as well as maximum effective perception range, offline time, offline times, etc.

[0075] Each type of evaluation indicator adopts the existing indicator measurement method at different road locations, different manufacturers, different roadside sensing devices, and different time periods to determine a value as the evaluation indicator of this type at the road location, the manufacturer, the roadside sensing device, and the time period.

[0076] Then, step S102 is executed to normalize the evaluation indicators of the intelligent connected roadside perception data quality evaluation indicator set to obtain a normalized indicator set, wherein the normalized indicator set includes the normalized evaluation indicators after each evaluation indicator is normalized.

[0077] Specifically, each evaluation index is normalized to a normalized value between 0 and 1. Each evaluation index is normalized to a normalized value between 0 and 1, which is a normalized evaluation index.

[0078] Finally, step S103 is executed to calculate the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions based on the normalized indicator set, and to identify the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality and / or the road location with the best roadside perception data quality based on the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions.

[0079] The present invention comprehensively considers a variety of evaluation indicators, comprehensively scores different equipment dimensions, manufacturer dimensions, and road position dimensions (road sections or intersections), and identifies the equipment, manufacturers, and road positions (road sections or intersections) with the best roadside perception data quality. A normalization processing method for intelligent connected roadside perception data quality evaluation indicators is proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the equipment dimension, and a method for identifying equipment with the best roadside perception data quality are proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the manufacturer dimension, and a method for identifying manufacturers with the best roadside perception data quality are proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the road position (road section or intersection) dimension, and a method for identifying road positions with the best roadside perception data quality are proposed. Compared with single indicator evaluation, the present invention comprehensively considers a variety of evaluation indicators and can more comprehensively evaluate the quality of roadside perception data.

[0080] The present invention uses a set of intelligent networked roadside perception data quality evaluation indicators, based on multiple evaluation indicators in the set of intelligent networked roadside perception data quality evaluation indicators, to comprehensively evaluate the quality of roadside perception equipment using multiple dimensions, and identifies the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality, and / or the road location with the best roadside perception data quality. Compared with a single indicator evaluation, the present invention comprehensively considers multiple evaluation indicators and can more comprehensively evaluate the quality of roadside perception data.

[0081] like Figure 2 The figure shows a workflow diagram of a method for evaluating quality of intelligent connected roadside perception data in another embodiment of the present invention, including:

[0082] Step S201, obtaining a set of quality evaluation indicators for intelligent connected roadside perception data, wherein the set of quality evaluation indicators for intelligent connected roadside perception data includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods.

[0083] Step S202, normalizing and judging the evaluation indicators in the intelligent connected roadside perception data quality evaluation indicator set:

[0084] If the evaluation index is a pass rate, the pass rate is used as a normalized evaluation index;

[0085] If the evaluation indicator is not the pass rate, and the evaluation indicator is a positive evaluation indicator, the positive evaluation indicator is normalized to a normalized value between 0 and 1 as the normalized evaluation indicator;

[0086] If the evaluation indicator is not the pass rate, and the evaluation indicator is a negative evaluation indicator, the evaluation indicator is converted into a positive evaluation indicator, and the converted evaluation indicator is normalized to a normalized value between 0 and 1 as a normalized evaluation indicator.

[0087] Step S203, based on the normalized indicator set, calculate the comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension, and identify the roadside perception device with the best roadside perception data quality.

[0088] Step S204, based on the normalized indicator set, calculate the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension, and identify the manufacturer with the best roadside perception data quality.

[0089] Step S205, based on the normalized indicator set, calculate the comprehensive evaluation index of the roadside perception data quality in the road position dimension, and identify the road position with the best roadside perception data quality.

[0090] Specifically, first execute step S201 to obtain a set of quality evaluation indicators for intelligent connected roadside perception data, wherein the set of quality evaluation indicators for intelligent connected roadside perception data includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods.

[0091] Specifically, the intelligent connected roadside perception data quality evaluation index set includes multiple evaluation indicators, including the track survival cycle qualification rate, speed qualification rate, etc., as well as the maximum value of the effective perception range, offline time, and number of offline times.

[0092] In some embodiments,

[0093] It represents the qualified rate of track survival cycle of equipment d of manufacturer j at road location i in the kth time period;

[0094] It represents the speed qualification rate of equipment d of manufacturer j at road position i in the kth time period;

[0095] …

[0096] It represents the maximum effective sensing range of device d of manufacturer j at road location i in the kth time period;

[0097] It indicates the offline time of the sensing device of manufacturer j at road location i in the kth period;

[0098] It represents the number of offline times of the sensing device of manufacturer j and device d at road location i in the kth time period;

[0099] The above indicators form the intelligent network roadside perception data quality evaluation index set, which is represented by I i,j,d,k It represents the quality evaluation index set of intelligent connected roadside perception data of manufacturer j device d at road location i in the kth period, that is,

[0100]

[0101] Then, step S202 is executed to normalize the evaluation indicators in the intelligent connected roadside perception data quality evaluation indicator set:

[0102] If the evaluation index is a pass rate, the pass rate is used as a normalized evaluation index;

[0103] If the evaluation indicator is not the pass rate, and the evaluation indicator is a positive evaluation indicator, the positive evaluation indicator is normalized to a normalized value between 0 and 1 as the normalized evaluation indicator;

[0104] If the evaluation indicator is not the pass rate, and the evaluation indicator is a negative evaluation indicator, the evaluation indicator is converted into a positive evaluation indicator, and the converted evaluation indicator is normalized to a normalized value between 0 and 1 as a normalized evaluation indicator.

[0105] Specifically, if the evaluation index is a qualified rate (such as the track survival period qualified rate, speed qualified rate, etc.), the value is between 0 and 1, and no normalization is required. The qualified rate is directly used as the normalized evaluation index;

[0106] If the evaluation index is not the pass rate, and the index is a positive evaluation index (such as the maximum value of the effective perception range), it is directly normalized to a normalized value between 0 and 1 as the normalized evaluation index.

[0107] If the evaluation indicator is not the pass rate, and the indicator is a negative evaluation indicator (such as offline time and number of offline times), it is necessary to first convert the indicator into a positive evaluation indicator, and then normalize it to a normalized value between 0 and 1 as a normalized evaluation indicator.

[0108] Specifically, positive indicators are needed to evaluate the optimal equipment, manufacturer, and road section location.

[0109] In some embodiments, the positive evaluation indicator includes a maximum effective perception range;

[0110] If the evaluation indicator is not the qualified rate, and the evaluation indicator is a positive evaluation indicator, the positive evaluation indicator is normalized to a normalized value between 0 and 1, including:

[0111] For each maximum value of the effective perception range, the following formula is used to normalize it to a normalized value between 0 and 1:

[0112]

[0113] In the formula, is the maximum effective sensing range of device d of manufacturer j at road location i in the kth period, express The normalized value of K represents the number of time periods within the duration T, N rcu Indicates the number of roadside sensing devices.

[0114] In one embodiment, converting the evaluation index into a positive evaluation index includes: calculating a set threshold value minus the evaluation index to obtain a converted evaluation index.

[0115] Specifically, when the evaluation indicator is a negative evaluation indicator, the set threshold is calculated to subtract the evaluation indicator to obtain a converted evaluation indicator, and the converted evaluation indicator is a positive evaluation indicator.

[0116] In some embodiments, the negative evaluation indicator includes offline time. By setting a threshold and subtracting the offline time, the converted evaluation indicator obtained is the online time, which is a positive evaluation indicator.

[0117] In some embodiments, the negative evaluation index includes the number of offline times. By setting a threshold value and subtracting the number of offline times, the converted evaluation index obtained is the number of online times, which is a positive evaluation index.

[0118] In some embodiments, if the evaluation indicator is not the pass rate and the evaluation indicator is a negative evaluation indicator, converting the evaluation indicator to a positive evaluation indicator, and normalizing the converted evaluation indicator to a normalized value between 0 and 1, includes:

[0119] For each offline duration, the offline duration is converted into a positive evaluation indicator using the following formula and then normalized to a normalized value between 0 and 1:

[0120]

[0121] In the formula, It represents the offline time of the roadside sensing device of manufacturer j and device d at road location i in the kth period, represents the normalized value of the offline time converted into a positive evaluation index, K represents the number of time periods within the time period T, and N rcu represents the number of roadside sensing devices, θ t Indicates the threshold for setting the online and offline time. For example, when each time period is 1 hour, θ t = 3600 seconds;

[0122] For each offline count, the following formula is used to convert the offline count into a positive evaluation indicator and then normalized to a normalized value between 0 and 1:

[0123]

[0124] In the formula, It represents the number of times the roadside sensing device of manufacturer j and device d at road location i is offline in the kth period. Represents the normalized value of the number of offline times converted to a positive evaluation index. n Indicates the threshold value for the number of online and offline times. For example, when each time period is 1 hour, θ n =60 times.

[0125] Specifically, the maximum effective sensing range The normalization method is as follows:

[0126]

[0127] In the formula, express The normalized value of K represents the number of time periods within the duration T, N rcu Indicates the number of sensing devices.

[0128] For the two negative evaluation indicators, offline time and offline times, the negative evaluation indicator is subtracted from the set threshold to obtain the positive evaluation indicator. This is equivalent to converting the offline time into the online time within the set time range, and converting the offline times into the online times within the set times range. If the offline time and offline times exceed the set threshold, they are directly set to 0.

[0129] Offline time The normalization method is as follows:

[0130]

[0131] In the formula, Represents the normalized positive evaluation index of offline time, that is, the normalized value of offline time converted into positive evaluation index (online time within the set time range). θ t Indicates the threshold for setting the online and offline time. For example, when each time period is 1 hour, θ t =3600 seconds.

[0132] Offline times The normalization method is as follows:

[0133]

[0134] In the formula, Represents the normalized positive evaluation index of offline times, that is, the normalized value of the offline times converted into the positive evaluation index (the online times within the set times range). θ n Indicates the threshold value for the number of online and offline times. For example, when each time period is 1 hour, θ n =60 times.

[0135] The duration T is the total test duration for determining the evaluation index. The duration T is divided into K time periods, and an evaluation index is determined for each time period k.

[0136] Finally, the above normalized indicators are combined into a normalized indicator set, where different road locations, different manufacturers, different equipment, and different time periods are combined, and each combination corresponds to a normalized indicator set. Represents the normalized indicator set of road location i, manufacturer j, equipment d at time period k. For example:

[0137]

[0138] Let x represent the normalized index set The xth index, N I Represents the normalized index set The number of indicators included. For example, in this embodiment, when x=1, When x=2, ...; when x = N I hour,

[0139] This embodiment proposes corresponding normalization methods for different evaluation indicators.

[0140] Then, execute steps S203-S205, calculate comprehensive evaluation indicators of roadside perception data quality in multiple dimensions based on the normalized indicator set, and identify the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality and / or the road location with the best roadside perception data quality based on the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions.

[0141] Among them, step S203 to step S205 are parallel steps.

[0142] Specifically, step S203 is performed to calculate the comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension based on the normalized index set, and identify the roadside perception device with the best roadside perception data quality.

[0143] In one of the embodiments, the calculating of the comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension based on the normalized index set, and identifying the roadside perception device with the best roadside perception data quality, includes:

[0144] Calculate a first roadside perception data quality evaluation index set in the roadside perception device dimension for each roadside perception device of each manufacturer at each road position, wherein each of the first roadside perception data quality evaluation index sets includes, in the normalized index set, an average value of similar normalized evaluation indicators of the roadside perception devices corresponding to the manufacturer corresponding to the road position corresponding to the first roadside perception data quality evaluation index set within a time length T;

[0145] The first comprehensive evaluation index of the quality of roadside perception data of each roadside perception device of each manufacturer at each road position in the roadside perception device dimension within the time length T is calculated as follows:

[0146]

[0147] In the formula, S i,j,d,T It represents the first comprehensive evaluation index of the quality of roadside perception data in the roadside perception device dimension of manufacturer j device d at road position i within time T. represents the first roadside perception data quality evaluation index set in the roadside perception device dimension of manufacturer j device d at road position i within time T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator;

[0148] Calculate the device with the best roadside perception data quality for manufacturer j at road position i within time T for:

[0149]

[0150] Where N rcu Indicates the number of roadside sensing devices;

[0151] Calculate the device with the best roadside perception data quality at road position i within time T for:

[0152]

[0153] Where N manuf Indicates the number of manufacturers.

[0154] Calculate the device with the best roadside perception data quality within the time T for:

[0155]

[0156] Where N road Indicates the number of road locations.

[0157] Specifically, firstly, a first roadside perception data quality evaluation index set in the roadside perception device dimension is calculated for each roadside perception device of each manufacturer at each road position. Each of the first roadside perception data quality evaluation index sets includes the normalized index set, and the average value of the same normalized evaluation index of the roadside perception device corresponding to the manufacturer corresponding to the road position corresponding to the first roadside perception data quality evaluation index set within a time length T.

[0158] Among them, the average value of the same type of normalized evaluation indicators of the roadside perception device corresponding to the manufacturer corresponding to the road position corresponding to the first roadside perception data quality evaluation indicator set within the time length T refers to: the normalized evaluation indicators of all time periods of the same type of the roadside perception device corresponding to the manufacturer corresponding to the road position corresponding to the first roadside perception data quality evaluation indicator set within the time length T are summed and divided by the number of time periods.

[0159] For example, with represents the first roadside perception data quality evaluation index set in the roadside perception device dimension of manufacturer j device d at road position i within time T, K represents the number of time periods within time T, then

[0160]

[0161] Let x represent the first roadside perception data quality evaluation index set The xth index, N I Represents the first roadside perception data quality evaluation index set The number of indicators included. For example, in this embodiment, when x=1, When x=2, ...; when x = N I hour,

[0162] Then, the first comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension is calculated for each roadside perception device of each manufacturer at each road position within the time length T.

[0163] Use S i,j,d,T It represents the comprehensive evaluation index of the first roadside perception data quality of the device d of manufacturer j at the road position i in the duration T. K represents the number of time periods within the duration T. The calculation formula is as follows:

[0164]

[0165] In the formula, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator. The above formula calculates the area of ​​the radar chart formed by the indicators.

[0166] In some embodiments, Right now

[0167] The distribution diagram of the first comprehensive evaluation index of roadside perception data quality in the equipment dimension is as follows: Figure 3 shown.

[0168] Finally, the identification process of the device with the best roadside perception data quality is as follows:

[0169] The first comprehensive evaluation index of roadside perception data quality in the above-mentioned equipment dimension is used to identify the equipment with the best roadside perception data quality.

[0170] The device with the best roadside perception data quality of manufacturer j at road position i within time T The calculation formula is as follows:

[0171]

[0172] Where N rcu Indicates the number of sensing devices.

[0173] The device with the best roadside perception data quality at road position i within time T The calculation formula is as follows:

[0174]

[0175] Where N manuf Indicates the number of manufacturers.

[0176] The device with the best roadside perception data quality within time T The calculation formula is as follows:

[0177]

[0178] Where N road Indicates the number of road locations.

[0179] This embodiment uses multiple evaluation indicators to determine the first comprehensive evaluation indicator of roadside perception data quality, and accurately identifies the device with the best roadside perception data quality through the first comprehensive evaluation indicator of roadside perception data quality.

[0180] Execute step S204 to calculate the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension based on the normalized index set, and identify the manufacturer with the best roadside perception data quality.

[0181] In one embodiment, the calculating of the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension based on the normalized index set and identifying the manufacturer with the best roadside perception data quality includes:

[0182] Calculate a second roadside perception data quality evaluation index set for each manufacturer at each road position in the manufacturer dimension, each of the second roadside perception data quality evaluation index sets including an average value of similar normalized evaluation indicators of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within a time length T in the normalized index set;

[0183] The comprehensive evaluation index of the second roadside perception data quality of each manufacturer at each road position in the manufacturer dimension within the duration T is calculated as follows:

[0184]

[0185] In the formula, S i,j,T It represents the comprehensive evaluation index of the second roadside perception data quality of manufacturer j at road position i in the manufacturer dimension within the duration T, represents the second roadside perception data quality evaluation index set of manufacturer j at road position i in the manufacturer dimension within time T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator;

[0186] Calculate the manufacturer with the best roadside perception data quality at road position i within time T for:

[0187]

[0188] The manufacturer with the best roadside perception data quality within the calculation time T for:

[0189]

[0190] Where N road Represents the number of road locations, N manuf Indicates the number of manufacturers.

[0191] Specifically, first, a second roadside perception data quality evaluation index set in the manufacturer dimension is calculated for each manufacturer at each road position, and each of the second roadside perception data quality evaluation index sets includes the normalized index set, and the average value of similar normalized evaluation indicators of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within a time length T.

[0192] Among them, the average value of the same type of normalized evaluation indicators of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within the time length T refers to: the normalized evaluation indicators of all roadside perception devices of the same type in all time periods of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within the time length T are summed and divided by the number of time periods and the number of perception devices.

[0193] For example, with represents the second roadside perception data quality evaluation index set of manufacturer j at road location i in duration T, K represents the number of time periods within duration T, N rcu represents the number of sensing devices, then

[0194]

[0195] Let x represent the second roadside perception data quality evaluation index set The xth index, N I Represents the second roadside perception data quality evaluation index set The number of indicators included. For example, in this embodiment, when x=1, When x=2, ...; when x = N I hour,

[0196] Then, the comprehensive evaluation index of the second roadside perception data quality in the manufacturer dimension is calculated for each manufacturer at each road position within the duration T.

[0197] Use S i,j,T It represents the comprehensive evaluation index of the second roadside perception data quality of manufacturer j at road position i within time T. The calculation formula is as follows:

[0198]

[0199] In the formula, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator. The above formula calculates the area of ​​the radar chart formed by the indicators.

[0200] In some embodiments, Right now

[0201] The schematic diagram of the distribution of the comprehensive evaluation index of the second roadside perception data quality in the manufacturer dimension is as follows Figure 4 shown.

[0202] Finally, the identification process of the manufacturer with the best roadside perception data quality is as follows:

[0203] The second comprehensive evaluation index of roadside perception data quality in the above-mentioned manufacturer dimension is used to identify the manufacturer with the best roadside perception data quality.

[0204] Manufacturer with the best roadside perception data quality at road position i within time T The solution formula is as follows:

[0205]

[0206] Manufacturer with the best roadside perception data quality within time T The solution formula is as follows:

[0207]

[0208] This embodiment uses multiple evaluation indicators to determine the second comprehensive evaluation indicator of roadside perception data quality, and accurately identifies the manufacturer with the best roadside perception data quality through the second comprehensive evaluation indicator of roadside perception data quality.

[0209] Execute step S205 to calculate the comprehensive evaluation index of the roadside perception data quality in the road position dimension based on the normalized index set, and identify the road position with the best roadside perception data quality.

[0210] In one of the embodiments, based on the normalized indicator set, a comprehensive evaluation index of the roadside perception data quality in the road position dimension is calculated, and the road position with the best roadside perception data quality is identified.

[0211] Calculating a third roadside perception data quality evaluation index set for each road position in the road position dimension, each of the third roadside perception data quality evaluation index sets includes an average value of similar normalized evaluation indicators of the road position corresponding to the third roadside perception data quality evaluation index set within a time length T in the normalized index set;

[0212] The third comprehensive evaluation index of the roadside perception data quality in the road position dimension within the time length T for each road position is calculated as:

[0213]

[0214] In the formula, S i,T represents the third comprehensive evaluation index of roadside perception data quality in the road position dimension within the time length T at the road position i, represents the third roadside perception data quality evaluation index set of road position i in the road position dimension within the time length T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator;

[0215] Calculate the road position with the best roadside perception data quality within the time period T for:

[0216]

[0217] Where N road Indicates the number of road locations.

[0218] Specifically, first, a third roadside perception data quality evaluation index set is calculated for each road position in the road position dimension, and each of the third roadside perception data quality evaluation index sets includes the normalized index set, and the average value of similar normalized evaluation indicators of the road position corresponding to the third roadside perception data quality evaluation index set within a time length T.

[0219] Among them, the average value of the same type of normalized evaluation indicators at the road position corresponding to the third roadside perception data quality evaluation indicator set within the time length T refers to: at the road position corresponding to the third roadside perception data quality evaluation indicator set within the time length T, the normalized evaluation indicators of all manufacturers of all roadside perception devices of the same type in all time periods are summed and divided by the number of time periods, the number of perception devices and the number of manufacturers.

[0220] For example, with represents the quality evaluation index set of intelligent connected roadside perception data at road position i within time T, K represents the number of time periods within time T, Nrcu Represents the number of sensing devices, N nanuf represents the number of manufacturers, then

[0221]

[0222] Let x represent the third roadside perception data quality evaluation index set The xth index, N I Represents the third roadside perception data quality evaluation index set The number of indicators included. For example, in this embodiment, when x=1, When x=2, ...; when x = N I hour,

[0223]

[0224] Then, the third comprehensive evaluation index of the roadside perception data quality in the road position dimension within the time length T is calculated for each road position.

[0225] Use S i,T It represents the comprehensive evaluation index of the third roadside perception data quality at the road position i within the time length T. The calculation formula is as follows:

[0226]

[0227] In the formula, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator. The above formula calculates the area of ​​the radar chart formed by the indicators.

[0228] In some embodiments, Right now

[0229] The schematic diagram of the distribution of the comprehensive evaluation index of the third roadside perception data quality in the road position dimension is as follows: Figure 5 shown.

[0230] Finally, the process of identifying the road location with the best roadside perception data quality is as follows:

[0231] The road position with the best roadside perception data quality within time T The solution formula is as follows:

[0232]

[0233] Where N road Indicates the number of road locations.

[0234] This embodiment uses multiple evaluation indicators to determine a third comprehensive evaluation indicator of roadside perception data quality, and accurately identifies the road location with the best roadside perception data quality through the third comprehensive evaluation indicator of roadside perception data quality.

[0235] It should be noted that the number of indicators N in the three dimensions I It may be the same or different.

[0236] The present invention comprehensively considers multiple evaluation indicators, comprehensively scores different equipment dimensions, manufacturer dimensions, and road position dimensions (road sections or intersections), and identifies the equipment, manufacturers, and road positions (road sections or intersections) with the best roadside perception data quality. A normalization processing method for intelligent connected roadside perception data quality evaluation indicators is proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the equipment dimension, and a method for identifying equipment with the best roadside perception data quality are proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the manufacturer dimension, and a method for identifying manufacturers with the best roadside perception data quality are proposed; a calculation method for comprehensive evaluation indicators of roadside perception data quality in the road position (road section or intersection) dimension, and a method for identifying road positions with the best roadside perception data quality are proposed. Compared with single indicator evaluation, the present invention comprehensively considers multiple evaluation indicators and can more comprehensively evaluate the quality of roadside perception data.

[0237] like Figure 6 The figure shows a working flow chart of a method for evaluating quality of intelligent connected roadside perception data according to a preferred embodiment of the present invention, including:

[0238] Step S601, determining a set of quality evaluation indicators for intelligent connected roadside sensing data;

[0239] Step S602, normalizing the intelligent connected roadside sensing data quality evaluation index set in step S601;

[0240] Step S603, using the normalized indicator set in step S602, calculating the comprehensive evaluation indicator of the roadside sensing data quality in the device dimension, and identifying the device with the best roadside sensing data quality;

[0241] Step S604, using the normalized indicator set in step S602, calculating the comprehensive evaluation indicator of the roadside sensing data quality in the manufacturer dimension, and identifying the manufacturer with the best roadside sensing data quality;

[0242] Step S605, using the normalized indicator set in step S602, calculate the comprehensive evaluation index of the roadside perception data quality in the road position dimension, and identify the road position (road section or intersection) with the best roadside perception data quality.

[0243] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0244] like Figure 7 The figure shows a hardware structure diagram of an electronic device of the present invention, including:

[0245] at least one processor 701; and,

[0246] A memory 702 that is communicatively connected to at least one of the processors 701; wherein,

[0247] The memory 702 stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the intelligent connected roadside perception data quality evaluation method as described above.

[0248] Figure 7 A processor 701 is taken as an example.

[0249] The electronic device may further include: an input device 703 and a display device 704 .

[0250] The processor 701, the memory 702, the input device 703 and the display device 704 may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0251] The memory 702, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the intelligent connected roadside perception data quality evaluation method in the embodiment of the present application, for example, Figure 1 , Figure 2 The processor 701 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 702, that is, the intelligent network roadside perception data quality evaluation method in the above embodiment is implemented.

[0252] The memory 702 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the intelligent network roadside perception data quality evaluation method, etc. In addition, the memory 702 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 702 may optionally include a memory remotely arranged relative to the processor 701, and these remote memories may be connected to a device for executing the intelligent network roadside perception data quality evaluation method via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0253] The input device 703 can receive user clicks and generate signal input related to user settings and function control of the intelligent network roadside perception data quality evaluation method. The display device 704 can include a display screen and other roadside perception display devices.

[0254] The one or more modules are stored in the memory 702, and when executed by the one or more processors 701, the intelligent connected roadside perception data quality evaluation method in any of the above method embodiments is executed.

[0255] The present invention uses a set of intelligent networked roadside perception data quality evaluation indicators, based on multiple evaluation indicators in the set of intelligent networked roadside perception data quality evaluation indicators, to comprehensively evaluate the quality of roadside perception data using multiple dimensions, and identifies the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality, and / or the road location with the best roadside perception data quality. Compared with a single indicator evaluation, the present invention comprehensively considers multiple evaluation indicators and can more comprehensively evaluate the quality of roadside perception data.

[0256] An embodiment of the present invention provides a storage medium, which stores computer instructions. When a computer executes the computer instructions, it is used to execute all steps of the intelligent connected roadside perception data quality evaluation method as described above.

[0257] In the context of the present disclosure, a storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, an apparatus, or a roadside sensing device. The storage medium may be a machine-readable signal medium or a machine-readable storage medium. Alternatively, the storage medium may be a non-temporary computer-readable storage medium, for example, a non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0258] An embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for evaluating the quality of intelligent connected roadside perception data as described above.

[0259] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating the quality of intelligent connected roadside perception data, characterized in that: include: Obtaining a set of quality evaluation indicators for intelligent networked roadside perception data, wherein the set of quality evaluation indicators for intelligent networked roadside perception data includes multiple types of evaluation indicators for different road locations, different manufacturers, different roadside perception devices, and different time periods; Normalizing the evaluation indicators of the intelligent connected roadside perception data quality evaluation indicator set to obtain a normalized indicator set, wherein the normalized indicator set includes a normalized evaluation indicator after each evaluation indicator is normalized; Based on the normalized indicator set, comprehensive evaluation indicators of roadside perception data quality in multiple dimensions are calculated, and according to the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions, the roadside perception equipment with the best roadside perception data quality, the manufacturer with the best roadside perception data quality and / or the road location with the best roadside perception data quality are identified.

2. The method for evaluating the quality of intelligent connected roadside perception data according to claim 1, characterized in that: The normalizing of the intelligent connected roadside perception data quality evaluation index set includes: The evaluation indicators in the intelligent connected roadside perception data quality evaluation indicator set are normalized and judged: If the evaluation index is a pass rate, the pass rate is used as a normalized evaluation index; If the evaluation indicator is not the pass rate, and the evaluation indicator is a positive evaluation indicator, the positive evaluation indicator is normalized to a normalized value between 0 and 1 as the normalized evaluation indicator; If the evaluation indicator is not the pass rate, and the evaluation indicator is a negative evaluation indicator, the evaluation indicator is converted into a positive evaluation indicator, and the converted evaluation indicator is normalized to a normalized value between 0 and 1 as a normalized evaluation indicator.

3. The method for evaluating quality of intelligent connected roadside perception data according to claim 2, characterized in that: The converting the evaluation index into a positive evaluation index includes: calculating a set threshold value minus the evaluation index to obtain a converted evaluation index.

4. The method for evaluating quality of intelligent connected roadside perception data according to claim 1, characterized in that: The step of calculating the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions based on the normalized indicator set, and identifying the roadside perception device with the best roadside perception data quality, the manufacturer with the best roadside perception data quality, and / or the road position with the best roadside perception data quality according to the comprehensive evaluation indicators of roadside perception data quality in multiple dimensions includes: Based on the normalized indicator set, calculating the comprehensive evaluation index of the roadside sensing data quality in the roadside sensing device dimension, and identifying the roadside sensing device with the best roadside sensing data quality; Based on the normalized indicator set, calculating the comprehensive evaluation index of the roadside perception data quality in the manufacturer dimension, and identifying the manufacturer with the best roadside perception data quality; Based on the normalized indicator set, a comprehensive evaluation index of the roadside perception data quality in the road position dimension is calculated, and the road position with the best roadside perception data quality is identified.

5. The method for evaluating quality of intelligent connected roadside perception data according to claim 4, characterized in that: The step of calculating the comprehensive evaluation index of the roadside perception data quality in the roadside perception device dimension based on the normalized index set and identifying the roadside perception device with the best roadside perception data quality includes: Calculating a first roadside perception data quality evaluation index set in the roadside perception device dimension for each roadside perception device of each manufacturer at each road position, wherein each of the first roadside perception data quality evaluation index sets includes, in the normalized index set, an average value of similar normalized evaluation indicators of the roadside perception device corresponding to the first roadside perception data quality evaluation index set within a time length T; The first comprehensive evaluation index of the quality of roadside perception data of each roadside perception device of each manufacturer at each road position in the roadside perception device dimension within the time length T is calculated as follows: In the formula, S i,j,d,T It represents the first comprehensive evaluation index of the quality of roadside perception data of device d of manufacturer j at road position i within time T in the roadside perception device dimension, represents the first roadside perception data quality evaluation index set of the device d of manufacturer j at road position i in the roadside perception device dimension within the time period T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator; Calculate the device with the best roadside perception data quality for manufacturer j at road position i within time T for: Where N rcu Indicates the number of roadside sensing devices; Calculate the device with the best roadside perception data quality at road position i within time T for: Where N manuf Indicates the number of manufacturers. Calculate the device with the best roadside perception data quality within the time T for: Where N road Indicates the number of road locations.

6. The method for evaluating quality of intelligent connected roadside perception data according to claim 4, characterized in that: The calculating, based on the normalized indicator set, a comprehensive evaluation indicator of the roadside perception data quality in the manufacturer dimension, and identifying the manufacturer with the best roadside perception data quality, includes: Calculate a second roadside perception data quality evaluation index set for each manufacturer at each road position in the manufacturer dimension, each of the second roadside perception data quality evaluation index sets including an average value of similar normalized evaluation indicators of the manufacturer corresponding to the road position corresponding to the second roadside perception data quality evaluation index set within a time length T in the normalized index set; The comprehensive evaluation index of the second roadside perception data quality of each manufacturer at each road position in the manufacturer dimension within the duration T is calculated as follows: In the formula, S i,j,T It represents the comprehensive evaluation index of the second roadside perception data quality of manufacturer j at road position i in the manufacturer dimension within the duration T, represents the second roadside perception data quality evaluation index set of manufacturer j at road position i in the manufacturer dimension within time T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator; Calculate the manufacturer with the best roadside perception data quality at road position i within time T for: The manufacturer with the best roadside perception data quality within the calculation time T for: Where N road Indicates the number of road locations, N manuf Indicates the number of manufacturers.

7. The method for evaluating quality of intelligent connected roadside perception data according to claim 4, characterized in that: Based on the normalized indicator set, a comprehensive evaluation index of the roadside perception data quality in the road position dimension is calculated, and the road position with the best roadside perception data quality is identified. Calculating a third roadside perception data quality evaluation index set for each road position in the road position dimension, each of the third roadside perception data quality evaluation index sets includes an average value of similar normalized evaluation indicators of the road position corresponding to the third roadside perception data quality evaluation index set within a time length T in the normalized index set; The third comprehensive evaluation index of the roadside perception data quality in the road position dimension within the time length T for each road position is calculated as: In the formula, S i,T represents the third comprehensive evaluation index of roadside perception data quality in the road position dimension within the time length T at the road position i, represents the third roadside perception data quality evaluation index set of the road position i in the road position dimension within the time length T, x represents the index number, N I express The number of indicators included, θ x,x+1 represents the angle between the xth index and the x+1th index, Indicates the Nth I The angle between the first indicator and the first indicator; Calculate the road position with the best roadside perception data quality within the time period T for: Where N road Indicates the number of road locations.

8. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the intelligent connected roadside perception data quality evaluation method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium stores computer instructions, which, when executed by a computer, are used to execute all steps of the intelligent connected roadside perception data quality evaluation method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for evaluating the quality of intelligent connected roadside perception data as described in any one of claims 1 to 7 is implemented.

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