An intelligent network connection roadside message packet loss rate detection method

Abstract

The application introduces an intelligent network connection roadside message packet loss rate detection method, including the following steps: step S1, using the Monte Carlo method to determine the maximum allowed packet loss rate O max ; step S2, using a data collection vehicle to collect the data broadcast by the roadside unit; step S3, calculating the data packet loss rate O broadcast by the roadside unit, and judging whether the data packet loss rate O broadcast by the roadside unit is qualified, if O max ≤O max , it is considered that the packet loss rate is qualified, if O>O max , it is considered that it is not qualified. Through the above design, the application can still realize accurate detection and judgment of the packet loss rate in the state that the roadside message is blocked.

Description

Technical Field

[0001] This invention belongs to the field of data detection, specifically relating to a method for detecting packet loss rate of intelligent connected roadside messages. Background Technology

[0002] Roadside messages refer to messages broadcast from roadside units installed on the side of open roads to the vehicle via a PC5 link. These messages typically include real-time traffic light messages, roadside safety messages, traffic event information, and traffic sign information maps. The vehicle receives and parses these roadside messages through pre-installed or aftermarket onboard units, and then transmits them to the connected vehicle decision and control unit, thus enabling the "data upload" of roadside messages to the vehicle.

[0003] Roadside messages often suffer from packet loss rates during transmission due to various reasons, directly leading to data unavailability and ultimately affecting the reliability of the entire vehicle-road cooperative system. Although existing methods for calculating packet loss rates are clearly defined, and there are also regulations on the allowable upper limit of packet loss rates, the measurement methods and standards still require relatively ideal conditions such as unobstructed roadside units. However, due to dynamic factors such as traffic congestion, it is not possible to ensure that the usage environment is completely unobstructed in actual data use.

[0004] To achieve "data onboarding," it is urgent to improve existing packet loss rate calculation methods and determine limits for packet loss rate under actual conditions, starting from the perspective of vehicle usage data. For example, Chinese patent CN102215144A discloses a packet loss rate measurement method. This method includes: a first end acquiring the frame sequence number of the received current measurement response frame and the frame sequence number of the previous measurement response frame, wherein the current measurement response frame and the previous measurement response frame are obtained by a second end responding to a packet loss rate measurement message frame sent by the first end; the first end determining whether the difference between the frame sequence number of the current measurement response frame and the frame sequence number of the previous measurement response frame is 1; if it is 1, the first end calculates the packet loss rate based on the counting information carried in the current measurement response frame and the previous measurement response frame; if it is greater than 1, the first end calculates the packet loss rate based on the difference between the counting information carried in the current measurement response frame and the previous measurement response frame and the frame sequence number. The above method can solve the problem of inaccurate packet loss rate measurement in the case of protocol frame loss in the existing technology. However, this method does not take into account the scenario of data interruption. In the actual field message collection process, the same RSU point may be passed by several times, and the time of each passage is different. In the end, the data collected from that point will be interrupted. This patent cannot be applied to the calculation of packet loss rate in such data collection scenarios.

[0005] Existing methods for calculating packet loss rate all require an unobstructed test environment. However, due to dynamic factors such as traffic congestion, roadside messages are difficult to achieve a completely unobstructed state, which leads to errors in the judgment of packet loss rate. Summary of the Invention

[0006] The technical problem this application aims to solve is that the existing V2X roadside message packet loss rate cap (packet loss rate cannot exceed 10%) is based on the premise of no obstruction. In reality, when there is obstruction, the packet loss rate generally far exceeds 10%. In actual operation and maintenance or data deployment environments, the 10% figure is meaningless. Therefore, under the condition of obstruction, a new allowable upper limit for packet loss rate needs to be determined.

[0007] A method for detecting packet loss rate of intelligent connected roadside messages includes the following steps:

[0008] Step S1: Determine the maximum allowable packet loss rate O using the Monte Carlo method. max ;

[0009] Step S2: Use a data acquisition vehicle to collect data broadcast from roadside units;

[0010] Step S3: Calculate the packet loss rate O of the data broadcast by the roadside unit;

[0011] Step S4: Determine whether the packet loss rate O of the data broadcast by the roadside unit is acceptable. If O≤O max If O>O, then the packet loss rate is considered acceptable. max Then it is considered unqualified.

[0012] Preferably, step S1 specifically includes the following steps:

[0013] Step S11: Through simulation and actual survey, simulate the actual data quality judgment black box f;

[0014] Step S12: Set the packet loss rate test range, and select the packet loss rate to be tested at equal intervals of 1%.

[0015] Step S13: Take a sufficient number of data samples without packet loss. Under the assumption that the packet loss rate and packet loss probability are the same, use different random seeds to simulate the data samples under each packet loss rate to be tested.

[0016] Step S14: Input the simulated data samples into the black box f, obtain the output corresponding to each data sample, and determine whether the roadside data samples are usable;

[0017] Step S15: Take the maximum packet loss rate of 10 data samples that all meet the requirements as the standard packet loss rate, denoted as Omax.

[0018] Preferably, step S2 specifically includes the following steps:

[0019] Step S21: Set up a data acquisition vehicle. Select an ordinary vehicle as the test vehicle and install the vehicle-mounted unit.

[0020] Step S22: Turn on the message receiving module of the vehicle unit, turn on the GNSS positioning module built into the vehicle unit, receive trajectory information, and perform time synchronization;

[0021] Step S23: Drive around the roadside unit according to the predetermined trajectory route, record the roadside message data to be tested, and record the local time and vehicle trajectory data when each message is received;

[0022] Step S24: After the data collection is completed, turn off the message receiving module and positioning module of the vehicle unit, and transfer all received roadside message data and driving trajectory data to the computer storage unit for offline analysis.

[0023] Preferably, step S3 specifically includes the following steps:

[0024] Step S31: Sort all roadside message data in ascending order of received timestamp;

[0025] Step S32: Subtract the timestamp of the previous data from the timestamp of the next data to obtain the time interval of the next data. The time interval of the first data is recorded as 0.

[0026] Step S33: Divide the data into segments according to time intervals. Set the data to be continuous when the time interval is less than or equal to 10 seconds. Filter the data with a time interval greater than 10 seconds as the starting point of each continuous data segment. Assume that a total of n continuous data segments can be obtained.

[0027] Step S34: Calculate the cumulative jump number l for each data segment. j and number of data entries s j , where j represents a segment of continuous data;

[0028] Step S35: Calculate the overall packet loss rate O.

[0029] Preferably, in step S35, the overall packet loss rate O is calculated as follows:

[0030] Preferably, in step S22, the trajectory information includes three fields: timestamp, longitude, and latitude, with a coordinate system of WGS-84, a minimum recording frequency of 1Hz, a time accuracy of milliseconds, and a positioning accuracy of meters.

[0031] Preferably, in step S13, the number of data samples without packet loss is greater than 10,000.

[0032] The advantages and effects of this application are as follows:

[0033] 1. This application presents a method for detecting packet loss rate of roadside messages in intelligent connected vehicles. First, a maximum allowable packet loss rate is determined using the Monte Carlo method. After collecting roadside messages from the field, the collected data is sorted according to timestamp size, and the data is segmented by setting a maximum time interval. The number of lost data points and the data volume of each segment are calculated separately, and then the overall packet loss rate is calculated and determined to be acceptable. Through this method, this application achieves accurate detection and judgment of packet loss rate even when roadside messages are obstructed.

[0034] 2. This application presents a method for detecting packet loss rate of intelligent connected roadside messages. The method segments data sent by the same roadside unit according to the receiving time interval, then filters out continuous valid data segments, calculates the number of lost packets and the actual number of received data items for each segment, and finally calculates the packet loss rate. Through this design, field roadside data collection personnel can set other data filtering conditions according to their needs and plan collection routes and schemes more freely and flexibly; thus, while being applicable to large-scale roadside message packet loss rate verification, it ensures the reliability of data measurement and improves the efficiency of daily packet loss rate inspection work.

[0035] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0036] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0038] Figure 1 A flowchart of a method for detecting packet loss rate of intelligent connected roadside messages provided in this application; Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0040] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0041] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0042] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0043] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0044] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0045] Example 1

[0046] This embodiment mainly introduces a method for detecting packet loss rate of intelligent connected roadside messages. Please refer to [link / reference needed]. Figure 1 It mainly includes the following steps:

[0047] Step S1: Determine the maximum allowable packet loss rate O using the Monte Carlo method. max ;

[0048] Step S2: Use a data acquisition vehicle to collect data broadcast from roadside units;

[0049] Step S3: Calculate the packet loss rate O of the data broadcast by the roadside unit;

[0050] Step S4: Determine whether the packet loss rate O of the data broadcast by the roadside unit is acceptable. If O≤O max If O>O, then the packet loss rate is considered acceptable. max Then it is considered unqualified.

[0051] Furthermore, step S1 specifically includes the following steps:

[0052] Step S11: Through simulation and actual survey, simulate the actual data quality judgment black box f;

[0053] Step S12: Set the packet loss rate test range. Select the packet loss rate to be tested at equal intervals of 1% between the minimum and maximum packet loss rates.

[0054] Step S13: Take a sufficient number of data samples without packet loss. Under the assumption that the packet loss rate and packet loss probability are the same, use different random seeds to simulate the data samples under each packet loss rate to be tested.

[0055] Step S14: Input the simulated data samples into the black box f, obtain the output corresponding to each data sample, and determine whether the roadside data samples are usable;

[0056] Step S15: Take the maximum packet loss rate of 10 data samples that all meet the requirements as the standard packet loss rate, denoted as Omax.

[0057] In this embodiment, the maximum allowable value of packet loss rate is first determined using the Monte Carlo method. Specifically, based on the actual data quality requirements, a data quality judgment black box is formed through simulation or modeling. The black box needs to determine whether the data is usable based on a given data sample. Different packet loss rates are selected to be tested, and then multiple data samples are generated for each packet loss rate using a random method. The samples are then input into the black box, which determines whether each data sample is usable.

[0058] Furthermore, step S2 specifically includes the following steps:

[0059] Step S21: Set up a data acquisition vehicle. Select an ordinary vehicle as the test vehicle and install the vehicle-mounted unit.

[0060] Step S22: Turn on the message receiving module of the vehicle unit, turn on the GNSS positioning module built into the vehicle unit, receive trajectory information, and perform time synchronization;

[0061] Step S23: Drive around the roadside unit according to the predetermined trajectory route, record the roadside message data to be tested, and record the local time and vehicle trajectory data when each message is received;

[0062] Step S24: After the data collection is completed, turn off the message receiving module and positioning module of the vehicle unit, and transfer all received roadside message data and driving trajectory data to the computer storage unit for offline analysis.

[0063] Furthermore, step S3 specifically includes the following steps:

[0064] Step S31: Sort all roadside message data in ascending order of received timestamp;

[0065] Step S32: Subtract the timestamp of the previous data from the timestamp of the next data to obtain the time interval of the next data. The time interval of the first data is recorded as 0.

[0066] Step S33: Divide the data into segments according to time intervals. Set the data to be continuous when the time interval is less than or equal to 10 seconds. Filter the data with a time interval greater than 10 seconds as the starting point of each continuous data segment. Assume that a total of n continuous data segments can be obtained.

[0067] Step S34: Calculate the cumulative jump number l for each data segment. j and number of data entries s j , where j represents a segment of continuous data;

[0068] Step S35: Calculate the overall packet loss rate O.

[0069] Furthermore, in step S35, the overall packet loss rate O is calculated as follows:

[0070] Furthermore, in step S22, the trajectory information includes three fields: timestamp, longitude, and latitude. The coordinate system is WGS-84, the minimum recording frequency is 1Hz, the time accuracy is milliseconds, and the positioning accuracy is meter-level.

[0071] Furthermore, in step S13, the number of data samples without packet loss is greater than 10,000.

[0072] This invention provides a method for detecting packet loss rate of intelligent connected roadside messages. Considering practical application needs, current standards for packet loss rate are based on ideal, unobstructed conditions such as laboratory settings. This invention establishes a packet loss rate standard framework for real-world application scenarios. Furthermore, considering the actual operation and maintenance needs of large-scale intelligent connected roadside equipment and facilities, the method segments data sent by the same roadside unit according to the receiving time interval, filters consecutive valid data segments, calculates the number of lost packets and the actual number of received data entries for each segment, and finally calculates the packet loss rate. The advantage of this method is that it not only meets existing packet loss rate calculation requirements but also allows roadside data collection personnel in the field to plan collection routes and schemes more freely and flexibly. It also facilitates setting other data filtering conditions according to needs, making it suitable for large-scale roadside message packet loss rate verification. While ensuring measurement reliability, it improves the efficiency of daily packet loss rate inspection work.

[0073] This application presents a method for detecting packet loss rate of roadside messages in intelligent connected vehicles. First, a Monte Carlo method is used to determine the maximum allowable packet loss rate. After collecting roadside messages from the field, the collected data is sorted by timestamp and segmented by setting a maximum time interval. The number of lost data points and the data volume of each segment are calculated separately, and then the overall packet loss rate is calculated and determined to be acceptable. Through this method, this application achieves accurate packet loss rate detection and judgment even when roadside messages are obstructed.

[0074] Example 2

[0075] Based on step S1 in Embodiment 1, this embodiment takes SPAT messages as an example and designs a Monte Carlo method to calculate the maximum allowable upper limit of packet loss rate.

[0076] Real-time traffic light messages are an important type of V2X roadside message. They can provide intelligent connected vehicles with the real-time status of traffic lights in all directions at the intersection ahead, as well as the current countdown information of the traffic lights. For driving safety, it is necessary to ensure that the countdown of the traffic lights does not jump. That is, the difference in the countdown in the same direction between two adjacent messages should not exceed 2 seconds. However, data will be lost during wireless transmission. The higher the packet loss rate, the greater the probability that the vehicle will receive a countdown jump.

[0077] Therefore, the algorithm for determining whether a SPAT message sample has a countdown transition is encapsulated into a black box f. f accepts SPAT message data samples with different packet loss rates as input and outputs the proportion of data in each sample that has a countdown transition. If the proportion exceeds a certain threshold, the data is considered unusable.

[0078] At a transmission frequency of 10Hz, 60,000 raw SPAT data packets without packet loss were collected to simulate the countdown transitions of SPAT data received by the OBU under different packet loss rates. Each data packet had an equal probability of being lost at the same packet loss rate. The packet loss rate was set within the range of 51% to 90%, with a sampling interval of 1%. Ten independent trials were conducted for each packet loss rate value to obtain ten samples. The proportion of samples exhibiting countdown transitions was output. The experimental results are shown in the table below.

[0079]

[0080]

[0081] Table 1 shows the experimental results of the ratio of countdown jumps for each sample.

[0082] If countdown transitions are not allowed (the percentage of countdown transitions is 0%), then the maximum allowable packet loss rate is 0.54.

[0083] Based on this, this application designs a method for detecting packet loss rate of intelligent connected roadside messages. The method segments data sent by the same roadside unit according to the receiving time interval, then filters out continuous valid data segments, calculates the number of lost packets and the actual number of received data items for each segment, and finally calculates the packet loss rate. Through this design, field roadside data collection personnel can set other data filtering conditions according to their needs and plan collection routes and schemes more freely and flexibly; thus, while being applicable to large-scale roadside message packet loss rate verification, it ensures the reliability of data measurement and improves the efficiency of daily packet loss rate inspection work.

[0084] The above description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter alterations to these embodiments within the spirit and principles of the present invention, achieved through conventional substitutions or by achieving the same function without departing from the principles and spirit of the present invention, fall within the scope of protection of the present invention.

Claims

1. A method for detecting packet loss rate of intelligent connected roadside messages, characterized in that, Includes the following steps: Step S1: Determine the maximum allowable packet loss rate using the Monte Carlo method. max ; Step S2: Use a data acquisition vehicle to collect data broadcast from roadside units; Step S3: Calculate the data packet loss rate of the roadside unit broadcast transmission to 0; Step S1 specifically includes the following steps: Step S11: Through simulation and actual survey, simulate the actual data quality judgment black box f; Step S12: Set the packet loss rate test range, and select the packet loss rate to be tested at equal intervals of 1% to 3%. Step S13: Take a sufficient number of data samples without packet loss. Under the assumption that the packet loss rate and packet loss probability are the same, use different random seeds to simulate the data samples under each packet loss rate to be tested. Step S14: Input the simulated data samples into the black box f, obtain the output corresponding to each data sample, and determine whether the roadside data samples are usable; Step S15: Take the maximum packet loss rate among 10 data samples that all meet the requirements as the standard packet loss rate, and denote it as 0. max ; In step S12, the packet loss rate test range is 51% to 90%, and the sampling interval is 1%. Step S2 specifically includes the following steps: Step S21: Set up a data acquisition vehicle. Select an ordinary vehicle as the test vehicle and install the vehicle-mounted unit. Step S22: Turn on the message receiving module of the vehicle unit, turn on the GNSS positioning module built into the vehicle unit, receive trajectory information, and perform time synchronization; Step S23: Drive around the roadside unit according to the predetermined trajectory route, record the roadside message data to be tested, and record the local time and vehicle trajectory data when each message is received; Step S24: After the data collection is completed, turn off the message receiving module and positioning module of the vehicle unit, and transfer all received roadside message data and driving trajectory data to the computer storage unit for offline analysis. Step S3 specifically includes the following steps: Step S31: Sort all roadside message data in ascending order of received timestamp; Step S32: Subtract the timestamp of the previous data from the timestamp of the next data to obtain the time interval of the next data. The time interval of the first data is recorded as 0. Step S33: Divide the data into segments according to time intervals. Set the data to be continuous when the time interval is less than or equal to 10 seconds. Filter the data with a time interval greater than 10 seconds as the starting point of each continuous data segment. Assume that a total of n continuous data segments can be obtained. Step S34: Calculate the cumulative jump number l for each data segment. j and number of data entries s j , where j represents a segment of continuous data; Step S35: Calculate the overall packet loss rate to 0; In step S35, the method for calculating the overall packet loss rate of 0 is as follows: .

2. The method for detecting packet loss rate of intelligent connected roadside messages according to claim 1, characterized in that, In step S22, the trajectory information includes three fields: timestamp, longitude, and latitude. The coordinate system is WGS-84, the minimum recording frequency is 1Hz, the time accuracy is milliseconds, and the positioning accuracy is meter-level.

3. The method for detecting packet loss rate of intelligent connected roadside messages according to claim 1, characterized in that, In step S13, the number of data samples without packet loss is greater than 10,000.

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