CN116170347A - Time-frequency performance test method for road side perception system of Internet of vehicles - Google Patents

Time-frequency performance test method for road side perception system of Internet of vehicles Download PDF

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CN116170347A
CN116170347A CN202211714316.8A CN202211714316A CN116170347A CN 116170347 A CN116170347 A CN 116170347A CN 202211714316 A CN202211714316 A CN 202211714316A CN 116170347 A CN116170347 A CN 116170347A
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time
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陈星筑
龚正
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Xintong Institute Innovation Center For Internet Of Vehicles Chengdu Co ltd
China Academy of Information and Communications Technology CAICT
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Xintong Institute Innovation Center For Internet Of Vehicles Chengdu Co ltd
China Academy of Information and Communications Technology CAICT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0852—Delays
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J3/00—Time-division multiplex systems
    • H04J3/02—Details
    • H04J3/06—Synchronising arrangements
    • H04J3/0635—Clock or time synchronisation in a network
    • H04J3/0638—Clock or time synchronisation among nodes; Internode synchronisation
    • H04J3/0647—Synchronisation among TDM nodes
    • H04J3/065—Synchronisation among TDM nodes using timestamps
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/50—Testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The invention discloses a time-frequency performance test method of a road side perception system of the Internet of vehicles, and belongs to the field of the Internet of vehicles. The test method comprises the following steps: s1: starting a test flow and data acquisition, and S2: processing and outputting test data, S3: and (5) analyzing and evaluating time-frequency performance. Compared with the prior art, the invention has the beneficial effects that: based on the truth vehicle data, the system data to be tested and the ROI data, the sensing time delay is calculated by selecting the test data with the geographic position characteristics, and the sensing time delay of the road side sensing system of the Internet of vehicles can be evaluated. It is also possible to evaluate whether the system frequency of the sensing system is stable by calculating TDEV and MTIE. The time-frequency performance of the road side sensing system is comprehensively reflected.

Description

Time-frequency performance test method for road side perception system of Internet of vehicles
Technical field:
the invention belongs to the field of Internet of vehicles, and particularly relates to a time-frequency performance test method of an Internet of vehicles road side perception system.
The background technology is as follows:
the vehicle-road cooperation is a safe, efficient and environment-friendly road traffic system which is formed by adopting advanced wireless communication, new generation internet and other technologies, implementing vehicle-vehicle and vehicle-road dynamic real-time information interaction in an omnibearing manner, developing vehicle active safety control and road cooperation management on the basis of full-time idle dynamic traffic information acquisition and fusion, fully realizing effective cooperation of human-vehicle roads, ensuring traffic safety and improving traffic efficiency.
The road side perception system (Roadside Sensing System, RSS) is an important means for supporting network automatic driving, improving traffic running efficiency and relieving congestion. The RSS system provides information such as beyond visual range perception, blind area early warning, driving intention and the like for the automatic driving automobile, and is one of important technical means for making up the limitation of the automatic driving perception of the bicycle.
In the vehicle-road cooperative application, the road side perception system realizes real-time vectorization and tracking of global targets, and the accurate perception capability is the key of the road side perception system.
Based on this, various approaches for evaluating the performance of road side aware systems have emerged in the art.
The prior patent with publication number of CN114383649A discloses a road side perception system testing method based on high-precision positioning, which comprises a testing system and a road side perception system, wherein the testing system comprises a mobile carrier, and the testing system takes the mobile carrier as a reference to acquire traffic participant information and outputs reference state information of the traffic participant information by processing the acquired traffic participant information; the road side perception system acquires the state information to be detected of the traffic participant; and comparing and analyzing the reference state information and the state information to be detected, calculating the error between the reference state information and the state information to be detected, and giving a performance evaluation report of the road side perception system according to the calculated error.
Another example of the prior patent with publication number CN112816954a discloses a road side perception system evaluating method based on true value, which comprises the following steps: establishing a true value sensing equipment group, and synchronously acquiring road side sensing data with sensing equipment of a road side sensing system RSS to be tested in a selected test time interval; processing the original data returned by the true value sensing equipment group to finish target type recognition and target track recognition and finish sensing data labeling; generating true values based on the marked data, wherein the true value data comprises the target type, position, speed, acceleration and track of the traffic participant; and in the selected test time interval, comparing the structured perception data output by the RSS to be tested with the truth data, and outputting a statistical evaluation result of the perception performance.
The prior patent with publication number of CN112382079A discloses a road side perception simulation method and system for vehicle road cooperation, which provides a virtual environment for simulation test before actual road test for road side perception, and can analyze the relation between the sensor before actual installation and the environment by means of the simulation environment to visually display the corresponding working effect.
The above patents all test and evaluate the perceptibility of the road side perception system by testing the perceptibility of the whole traffic participants, including traffic targets, traffic events, traffic flows, etc., but cannot evaluate the time-frequency performance of the road side perception system to a certain extent.
The time-frequency performance of the road side perception system is particularly important to test and evaluate besides reflecting the stability of the system itself, which is one of important factors affecting the actual perception effect.
Disclosure of Invention
In order to solve the problems, the primary purpose of the invention is to provide a time-frequency performance test method for a road side perception system of the internet of vehicles, which is based on truth vehicle data, system data to be tested and ROI data, calculates perception time delay by selecting test data with geographic position characteristics, and can evaluate the perception time delay of the road side perception system of the internet of vehicles.
The invention further aims to provide a time-frequency performance test method of the road side perception system of the Internet of vehicles, which can evaluate whether the system frequency of the perception system is stable or not by calculating TDEV and MTIE.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a time-frequency performance test method of a road side perception system of an Internet of vehicles comprises the following steps:
s1: starting a test flow and data acquisition: determining the ROI range of a detection area, selecting a truth vehicle to perform running test in the detection area, recording the truth data of the own vehicle by the truth vehicle, performing target perception on the truth vehicle of the detection area by a system to be detected, and outputting structured perception data;
s2: processing and outputting test data: the true value vehicle and the system to be tested process the sensing data, the sensing time stamp ts, the MEC sending time stamp tt and the data of target object information corresponding to the sensing time stamp tt and the MEC sending time stamp tt are output, the data output by the true value vehicle is Sgt, the data output by the system to be tested are Sduc 1 and Sduc 2, wherein the content in the Sduc 1 data comprises the sensing data of the system to be tested on all targets in the sensing range, and the Sduc 2 only comprises the sensing data of the true value vehicle;
s3: and (5) analyzing and evaluating time-frequency performance.
Further, the step S1 includes:
step S11: the extent of the test area is determined. Recording the longitude and latitude information of each point position according to the sequence of southeast-east-northeast-north-northwest-west-southwest-south-southeast;
step S12: selecting a truth vehicle, selecting a place beyond the perception range of a system to be detected as a starting point of the truth vehicle, starting from the starting point, testing the truth vehicle and recording truth data of the running of the vehicle;
step S13: the truth vehicle runs at normal urban traffic running speed, enters along the lane from the entrance direction and passes through the delimited test area;
step S14: the truth value vehicle leaves the test area and stops the truth value data acquisition of the own vehicle;
step S15: if the test area contains turning roads, repeating S11-S13 until all lanes are traversed, and driving along the lanes regularly to finish the route (namely, straight running and straight running, turning around a curve, if the lanes can simultaneously go straight and turn around, then, going straight first, and executing turning again for the next test);
if only straight lanes exist in the test area, repeating the steps S11-S13 until all lanes are traversed, repeating the steps S11-S13 at least 2 times, and requiring that the truth vehicle runs in a curve S shape in the step S12 repeated additionally and passes through the test area in a movement mode crossing at least 2 lanes; in the application, the truth vehicle runs in a curve S shape and repeats the steps S11-S13, so that the data of vehicle turning can be acquired in a test scene of a long straight road, and the completeness of the acquired data is ensured.
Further, in step S12, the truth vehicle is outside the sensing range of the system to be tested, and the distance between the truth vehicle and the position of the system to be tested is required to be more than 400 meters.
Further, in step S13, the traveling speed of the truth vehicle is 20-60km/h.
Further, in step S14, after the truth vehicle leaves the test area, the vehicle needs to travel an additional distance of at least 150 meters, and then the data acquisition of the vehicle is stopped to ensure the integrity of the truth data.
Further, in step S2, the sensing timestamp is a time when the sensor device collects the target object information, where the sensor device includes a sensor device of the truth vehicle and a sensor device of the sensing system. The MEC transmit timestamp tt is: and the sensing system processes the acquired target object information through the MEC and then externally outputs the time of the sensing result.
Further, in step S2, the target information includes, but is not limited to, target longitude, latitude, altitude, speed, heading angle.
Further, in step S2, the output Sgt, sdut1 and Sdut2 after each test are in one-to-one correspondence, and are defined as a set of test set data.
Further, the step S3 includes:
s31: positioning reference points of the system to be tested and the system to be tested are unified on one path side;
s32: checking the data of Sgt, sduc 1 and Sduc 2, reserving effective data, eliminating ineffective data, and drawing a track diagram of Sgt and Sduc 2;
s33: selecting data with geographic position characteristics as test group data;
s34: calculating the mean value Deltat of the difference between the MEC sending time stamp tt and the sensing time stamp ts of each frame from the Sdut2 data of the system to be tested, namely Deltat=mean (tt-ts);
s35: based on the sensing time stamp ts, extracting continuous position information in Sgt and Sdut2, and setting the continuous position information as track data Pgt and Pdut respectively;
s36: setting the transformed Pdut track as
Figure SMS_1
Wherein R is 0 And t 0 The track rotation matrix and the track translation matrix are respectively;
s37: searching the transformed Pdut track by a KNN searching method
Figure SMS_2
The shortest Euclidean distance between each point in the track Pgt and the point, and the Euclidean distance is defined as a residual error delta ag ；
The expression is
Figure SMS_3
S38: optimizing the residual delta by L-M optimization ag To obtain a minimum delta ag And calculates the corresponding R * And t * ；
S39: based on the result of the last step, the system track to be measured after being fitted with the Pgt track is obtained as follows:
Figure SMS_4
s310: calculation of
Figure SMS_5
Time error between corresponding points of Pgt, time error is +.>
Figure SMS_6
The difference value between the corresponding sensing time stamp ts and the corresponding sensing time stamp ts of Pgt is averaged, and the average value delta t is obtained;
s311: calculating a perception time delay Dsen= delta t+delta t;
s312: TDEV is calculated based on tt time stamps in the Sdut1 data. TDEV is a measure of the amount of change in signal time interval over a given integration time τ, which can provide information about the signal phase and spectral content. Setting ideal sampling interval of system to be tested as tau 0 ＝1/f obj Wherein f obj For the default frequency of the system to be tested, when the observation interval is τ=nτ 0 When TDEV is estimated by:
Figure SMS_7
wherein:
Figure SMS_8
-rounding down; />
x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval;
τ—integration time;
n-number of sampling intervals within the integration time.
S313: MTIE is calculated based on tt time stamps in the Sdut1 data. MTIE is the total length of measurement period T for a given observation interval τ=nτ 0 The maximum value of the peak value of the delay time between the time interval of the timing signal output by the downstream side sensing system and the ideal time interval characterizes the stability of the frequency. It can be estimated by the following formula:
Figure SMS_9
wherein: x is x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval.
Further, in step S31, the positioning reference point is the origin of the local tangential plane coordinate system, and is used to determine the coordinate position of the truth vehicle.
Further, in step S32, the judgment conditions of the effective data are: the tracks of Sgt and Sduc 2 are continuous and have no obvious interruption, and the time interval of Sgt completely covers the time intervals of Sduc 1 and Sduc 2; otherwise, the data is taken as invalid data. The Sgt and Sdut2 track diagrams are drawn, so that the visualization of Sgt and Sdut2 data can be realized, and whether obvious deviation and interruption occur to the tracks of the Sgt and the Sdut2 is conveniently found.
Further, in step S33, each test group data includes the Sgt, sdut1, and Sdut2 under the same sensing time stamp.
Further, in step S33, the data with the geographic location feature is data with the cornering behavior of the vehicle, including curve test data or "S-shaped" test data, so as to ensure the integrity of the test data.
Further, in step S36, two points on any position of the Pdut locus may be mapped by the locus rotation and translation.
Further in step S38, R * And t * R in step S36 0 And t 0 The specific numerical value corresponding to the method.
Further, in step S312, the observation interval is τ=nτ 0 In, the smaller the value of TDEV, the more stable the frequency of the perception system; in step S313, the observation interval τ=nτ 0 In this, the smaller the value of MTIE, the more stable the frequency of the perception system.
Compared with the prior art, the invention has the beneficial effects that: based on the truth vehicle data, the system data to be tested and the ROI data, the sensing time delay is calculated by selecting the test data with the geographic position characteristics, and the sensing time delay of the road side sensing system of the Internet of vehicles can be evaluated. It is also possible to evaluate whether the system frequency of the sensing system is stable by calculating TDEV and MTIE. The time-frequency performance of the road side sensing system is comprehensively reflected.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention is realized as follows:
a time-frequency performance test method of a road side perception system of an Internet of vehicles comprises the following steps:
s1: starting a test flow and data acquisition: determining the ROI range of a detection area, selecting a truth vehicle to perform running test in the detection area, recording the truth data of the own vehicle by the truth vehicle, performing target perception on the truth vehicle of the detection area by a system to be detected, and outputting structured perception data;
s2: processing and outputting test data: the true value vehicle and the system to be tested process the sensing data, the sensing time stamp ts, the MEC sending time stamp tt and the data of target object information corresponding to the sensing time stamp tt and the MEC sending time stamp tt are output, the data output by the true value vehicle is Sgt, the data output by the system to be tested are Sduc 1 and Sduc 2, wherein the content in the Sduc 1 data comprises the sensing data of the system to be tested on all targets in the sensing range, and the Sduc 2 only comprises the sensing data of the true value vehicle;
s3: and (5) analyzing and evaluating time-frequency performance.
Further, the step S1 includes:
step S11: the extent of the test area is determined. Recording the longitude and latitude information of each point position according to the sequence of southeast-east-northeast-north-northwest-west-southwest-south-southeast;
step S12: selecting a truth vehicle, selecting a place beyond the perception range of a system to be detected as a starting point of the truth vehicle, starting from the starting point, testing the truth vehicle and recording truth data of the running of the vehicle;
step S13: the truth vehicle runs at normal urban traffic running speed, enters along the lane from the entrance direction and passes through the delimited test area;
step S14: the truth value vehicle leaves the test area and stops the truth value data acquisition of the own vehicle;
step S15: if the test area contains turning roads, repeating S11-S13 until all lanes are traversed, and driving along the lanes regularly to finish the route (namely, straight running and straight running, turning around a curve, if the lanes can simultaneously go straight and turn around, then, going straight first, and executing turning again for the next test);
if only straight lanes exist in the test area, repeating the steps S11-S13 until all lanes are traversed, repeating the steps S11-S13 at least 2 times, and requiring that the truth vehicle runs in a curve S shape in the step S12 repeated additionally and passes through the test area in a movement mode crossing at least 2 lanes; in the application, the truth vehicle runs in a curve S shape and repeats the steps S11-S13, so that the data of vehicle turning can be acquired in a test scene of a long straight road, and the completeness of the acquired data is ensured.
Further, in step S12, the truth vehicle is outside the sensing range of the system to be tested, and the distance between the truth vehicle and the position of the system to be tested is required to be more than 400 meters.
Further, in step S13, the traveling speed of the truth vehicle is 20-60km/h.
Further, in step S14, after the truth vehicle leaves the test area, the vehicle needs to travel an additional distance of at least 150 meters, and then the data acquisition of the vehicle is stopped to ensure the integrity of the truth data.
Further, in step S2, the sensing timestamp is a time when the sensor device collects the target object information, where the sensor device includes a sensor device of the truth vehicle and a sensor device of the sensing system. The MEC transmit timestamp tt is: and the sensing system processes the acquired target object information through the MEC and then externally outputs the time of the sensing result.
Further, in step S2, the target information includes, but is not limited to, target longitude, latitude, altitude, speed, heading angle.
Further, in step S2, the output Sgt, sdut1 and Sdut2 after each test are in one-to-one correspondence, and are defined as a set of test set data. The data definitions for Sgt, sdut1 and Sdut2 are shown in Table 1 below:
table 1:
Figure SMS_10
Figure SMS_11
further, the step S3 includes:
s31: positioning reference points of the system to be tested and the system to be tested are unified on one path side;
s32: checking the data of Sgt, sduc 1 and Sduc 2, reserving effective data, eliminating ineffective data, and drawing a track diagram of Sgt and Sduc 2;
s33: selecting data with geographic position characteristics as test group data;
s34: calculating the mean value Deltat of the difference between the MEC sending time stamp tt and the sensing time stamp ts of each frame from the Sdut2 data of the system to be tested, namely Deltat=mean (tt-ts);
s35: based on the sensing time stamp ts, extracting continuous position information in Sgt and Sdut2, and setting the continuous position information as track data Pgt and Pdut respectively;
s36: setting the transformed Pdut track as
Figure SMS_12
Wherein R is 0 And t 0 The track rotation matrix and the track translation matrix are respectively;
s37: searching the transformed Pdut track by a KNN searching method
Figure SMS_13
The shortest Euclidean distance between each point in the track Pgt and the point, and the Euclidean distance is defined as a residual error delta ag ；
The expression is
Figure SMS_14
S38: optimizing the residual delta by L-M optimization ag To obtain a minimum delta ag And calculates the corresponding R * And t * ；
S39: based on the result of the last step, the system track to be measured after being fitted with the Pgt track is obtained as follows:
Figure SMS_15
s310: calculation of
Figure SMS_16
Time error between corresponding points of Pgt, time error is +.>
Figure SMS_17
The difference value between the corresponding sensing time stamp ts and the corresponding sensing time stamp ts of Pgt is averaged, and the average value delta t is obtained;
s311: calculating a perception time delay Dsen= delta t+delta t;
s312: TDEV is calculated based on tt time stamps in the Sdut1 data. TDEV is a measure of the amount of change in signal time interval over a given integration time τ, which can provide information about the signal phase and spectral content. Setting ideal sampling interval of system to be tested as tau 0 ＝1/f obj Wherein f obj For the default frequency of the system to be tested, when the observation interval is τ=nτ 0 When TDEV is estimated by:
Figure SMS_18
wherein:
Figure SMS_19
-rounding down;
x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval;
τ—integration time;
n-number of sampling intervals within the integration time.
S313: MTIE is calculated based on tt time stamps in the Sdut1 data. MTIE is the total length of measurement period T for a given observation interval τ=nτ 0 Peak of delay time between timing signal time interval and ideal time interval output by downstream side sensing systemThe maximum of the values characterizes the stability of the frequency. It can be estimated by the following formula:
Figure SMS_20
wherein: x is x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval.
Further, in step S31, the positioning reference point is the origin of the local tangential plane coordinate system, and is used to determine the coordinate position of the truth vehicle.
Further, in step S32, the judgment conditions of the effective data are: the tracks of Sgt and Sduc 2 are continuous and have no obvious interruption, and the time interval of Sgt completely covers the time intervals of Sduc 1 and Sduc 2; otherwise, the data is taken as invalid data. The Sgt and Sdut2 track diagrams are drawn, so that the visualization of Sgt and Sdut2 data can be realized, and whether obvious deviation and interruption occur to the tracks of the Sgt and the Sdut2 is conveniently found.
Further, in step S33, each test group data includes the Sgt, sdut1, and Sdut2 under the same sensing time stamp.
Further, in step S33, the data with the geographic location feature is data with the cornering behavior of the vehicle, including curve test data or "S-shaped" test data, so as to ensure the integrity of the test data.
Further, in step S36, two points on any position of the Pdut locus may be mapped by the locus rotation and translation.
Further in step S38, R * And t * R in step S36 0 And t 0 The specific numerical value corresponding to the method.
Further, in step S312, the observation interval is τ=nτ 0 In, the smaller the value of TDEV, the more stable the frequency of the perception system; in step S313, the observation interval τ=nτ 0 In this, the smaller the value of MTIE, the more stable the frequency of the perception system.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.

Claims (10)

1. The time-frequency performance test method for the road side perception system of the Internet of vehicles is characterized by comprising the following steps of:
s1: starting a test flow and data acquisition: determining the ROI range of a detection area, selecting a truth vehicle to perform running test in the detection area, recording the truth data of the own vehicle by the truth vehicle, performing target perception on the truth vehicle of the detection area by a system to be detected, and outputting structured perception data;
s2: processing and outputting test data: the true value vehicle and the system to be tested process the sensing data, the sensing time stamp ts, the MEC sending time stamp tt and the data of target object information corresponding to the sensing time stamp tt and the MEC sending time stamp tt are output, the data output by the true value vehicle is Sgt, the data output by the system to be tested is Sduc 1 and Sduc 2, the content in the Sduc 1 data comprises the sensing data of the system to be tested on all targets in the sensing range, and the Sduc 2 only comprises the sensing data of the true value vehicle;
s3: and (5) analyzing and evaluating time-frequency performance.
2. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 1, wherein the step S1 comprises:
step S11: the extent of the test area is determined. Recording the longitude and latitude information of each point position according to the sequence of southeast-east-northeast-north-northwest-west-southwest-south-southeast;
step S12: selecting a truth vehicle, selecting a place beyond the perception range of a system to be detected as a starting point of the truth vehicle, starting from the starting point, testing the truth vehicle and recording truth data of the running of the vehicle;
step S13: the truth vehicle runs at normal urban traffic running speed, enters along the lane from the entrance direction and passes through the delimited test area;
step S14: the truth value vehicle leaves the test area and stops the truth value data acquisition of the own vehicle;
step S15: if the test area contains turning roads, repeating S11-S13 until all lanes are traversed, and driving along the lanes regularly to finish the route (namely, straight running and straight running, turning around a curve, if the lanes can simultaneously go straight and turn around, then, going straight first, and executing turning again for the next test);
if only straight lanes exist in the test area, repeating the steps S11-S13 until all lanes are traversed, repeating the steps S11-S13 at least 2 times, and requiring that the truth vehicle runs in a curve S shape in the step S12 repeated additionally and passes through the test area in a movement mode crossing at least 2 lanes; in the application, the truth vehicle runs in a curve S shape and repeats the steps S11-S13, so that the data of vehicle turning can be acquired in a test scene of a long straight road, and the completeness of the acquired data is ensured.
3. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 1, wherein in step S2, the target information includes, but is not limited to, target longitude, latitude, altitude, speed, heading angle.
4. The method for testing time-frequency performance of a road side perception system of an internet of vehicles according to claim 1, wherein in step S2, the Sgt, the Sdut1 and the Sdut2 outputted after each test are in one-to-one correspondence, and are defined as a set of test set data.
5. The method for testing time-frequency performance of the road side perception system of the internet of vehicles according to claim 1, wherein the step S3 comprises:
s31: positioning reference points of the system to be tested and the system to be tested are unified on one path side;
s32: checking the data of Sgt, sduc 1 and Sduc 2, reserving effective data, eliminating ineffective data, and drawing a track diagram of Sgt and Sduc 2;
s33: selecting data with geographic position characteristics as test group data;
s34: calculating the mean value Deltat of the difference between the MEC sending time stamp tt and the sensing time stamp ts of each frame from the Sdut2 data of the system to be tested, namely Deltat=mean (tt-ts);
s35: based on the sensing time stamp ts, extracting continuous position information in Sgt and Sdut2, and setting the continuous position information as track data Pgt and Pdut respectively;
s36: setting the transformed Pdut track as
Figure FDA0004027311970000021
Wherein R is 0 And t 0 The track rotation matrix and the track translation matrix are respectively;
s37: searching the transformed Pdut track by a KNN searching method
Figure FDA0004027311970000022
The shortest Euclidean distance between each point in the track Pgt and the point, and the Euclidean distance is defined as a residual error delta ag ；
The expression is
Figure FDA0004027311970000023
S38: optimizing the residual delta by L-M optimization ag To obtain a minimum delta ag And calculates the corresponding R * And t * ；
S39: based on the result of the last step, the system track to be measured after being fitted with the Pgt track is obtained as follows:
Figure FDA0004027311970000031
s310: calculation of
Figure FDA0004027311970000032
Time error between corresponding points of Pgt, time error is +.>
Figure FDA0004027311970000033
Corresponding toThe difference between the sensing time stamps ts corresponding to the sensing time stamps ts and Pgt is averaged, and the average value delta t is obtained;
s311: the perceived delay dsen= Δt+δt is calculated.
6. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 5, wherein in step S32, the judging condition of the effective data is: the tracks of Sgt and Sduc 2 are continuous and have no obvious interruption, and the time interval of Sgt completely covers the time intervals of Sduc 1 and Sduc 2; otherwise, the data is taken as invalid data.
7. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 5, wherein in step S33, the data with geographical location characteristics is data with vehicle cornering behavior, including curve test data or "S-shaped" test data.
8. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 5, wherein in step S38, R * And t * R in step S36 0 And t 0 The specific numerical value corresponding to the method.
9. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 5, wherein step S3 further comprises S312: calculating a TDEV based on MEC sending time stamp tt in Sdut1 data; TDEV is a measure of the variation of signal time interval within a given integration time tau, and sets the ideal sampling interval of the system under test to be tau 0 ＝1/f obj Wherein f obj For the default frequency of the system to be tested, when the observation interval is τ=nτ 0 When the TDEV is estimated, the formula is as follows:
Figure FDA0004027311970000034
wherein:
Figure FDA0004027311970000035
-rounding down;
x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval;
τ—integration time;
n-number of sampling intervals within the integration time.
10. The method for testing time-frequency performance of a road side perception system of internet of vehicles according to claim 5, wherein step S3 further comprises S313: MTIE is calculated based on MEC transmission time stamp tt in the Sdut1 data, MTIE being for a given observation interval τ=nτ during a measurement period of total length T 0 The maximum value of the peak value of the delay time between the time interval of the timing signal output by the downstream side sensing system and the ideal time interval is estimated by the following formula:
Figure FDA0004027311970000041
wherein: x is x i -instantaneous time error;
n—total sample amount;
τ 0 -sampling interval.
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Publication number Priority date Publication date Assignee Title
CN117880820A (en) * 2024-03-12 2024-04-12 南京纳特通信电子有限公司 Radio safety guarantee system and method for Internet of vehicles

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117880820A (en) * 2024-03-12 2024-04-12 南京纳特通信电子有限公司 Radio safety guarantee system and method for Internet of vehicles
CN117880820B (en) * 2024-03-12 2024-05-17 南京纳特通信电子有限公司 Radio safety guarantee system and method for Internet of vehicles

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