CN112766766A - High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof - Google Patents

High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof Download PDF

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CN112766766A
CN112766766A CN202110101606.4A CN202110101606A CN112766766A CN 112766766 A CN112766766 A CN 112766766A CN 202110101606 A CN202110101606 A CN 202110101606A CN 112766766 A CN112766766 A CN 112766766A
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唐洁
李东华
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South China University of Technology SCUT
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Abstract

The invention discloses a high-precision map crowdsourcing system based on an optimal stop-and-go rule and a data collecting method thereof. The task issuing module is used for issuing a high-precision map crowdsourcing task; the member preference module is used for selecting a winning participant from the candidate participant set, receiving data of the winning participant and providing consideration for the winning participant; the optimal time stopping module controls the stopping time of the high-precision map crowdsourcing task to maximize crowdsourcing utility. Based on the system, a data collection method of the high-precision map crowdsourcing system based on the optimal time-stop rule is provided. The method provides sufficient data sources for updating the high-precision map, reduces the cost of data collection, and enables the updating of the high-precision map to be more real-time and efficient.

Description

High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof
Technical Field
The invention relates to the technical field of urban intelligent traffic, in particular to a high-precision map crowdsourcing system based on an optimal stop-and-go rule and a data collection method thereof.
Background
In the development of the existing unmanned vehicle, sensing is used as an important module in the unmanned vehicle, and is a precondition that the unmanned vehicle can safely run on the road. Sensors such as cameras and laser radars are the mainstream means for the unmanned vehicle to sense the surrounding environment. Sensing the surroundings by sensors, however, presents significant challenges in terms of cost. The high-precision map as an emerging perception means well makes up the defect of high cost of a sensor, so how to conveniently and quickly acquire and update the high-precision map is the current main task. In order to collect high-precision map data, map businessmen and unmanned vehicle businessmen such as Baidu, Goods and TomTom have already built a professional map collection fleet belonging to the map businessmen and the unmanned vehicle businessmen, and street view image data and 3D laser point cloud data are obtained through a high-precision map collection vehicle provided with a camera and a laser radar. And the stored source data is subjected to a background automatic map building process, and a high-precision map is produced and issued by combining manual error correction and marking. Although the data acquisition operation precision of the professional acquisition vehicle is high, the road information acquisition is comprehensive, the construction cost is quite high, the quantity is small, the operation period is long, and the map output is limited. A method of updating a high-precision map in real time by collecting a vehicle is not practical.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a high-precision map crowdsourcing system based on an optimal time-stop rule and a data collection method thereof.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: high-precision map crowdsourcing system based on optimal time-stop rules, comprising:
the task issuing module is used for issuing a high-precision map crowdsourcing task;
the member optimization module is used for realizing selection of a winning participant in the candidate participant set, receiving data of the winning participant and providing consideration for the winning participant;
and the optimal time stopping module is used for controlling the stopping time of the high-precision map crowdsourcing task so as to maximize crowdsourcing utility.
Further, the task issuing module comprises a traffic flow prediction module, a time division module and an issuing module, wherein:
the traffic flow prediction module is responsible for predicting the current traffic flow through the historical data of the road network;
the time division module is responsible for dispersing time into a time slice form, and calculating the length of the time slice and the longest execution time of a high-precision map crowdsourcing task according to a traffic flow prediction result;
the issuing module is responsible for issuing the high-precision map crowdsourcing task according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected selection number of participants in a unit time slice, and in addition, a reward is given.
Further, the member preference module includes a scoring module, a selection module, and a reward module, wherein:
the score module is responsible for calculating the score of each candidate participant according to the vehicle-mounted sensor equipment information and the reputation value of the candidate participant in the current time slice;
the selection module is responsible for selecting a winning participant according to the score of each candidate participant in the current time slice and receiving data collected by the winning participant;
the reward module is responsible for providing a reward to the winning participant.
Further, the optimal time-out module comprises a crowdsourcing state update module and a stop decision module, wherein:
the crowdsourcing state updating module is responsible for updating the current accumulated collected data volume and updating the crowdsourcing state according to the current accumulated collected data volume;
and the stopping decision module is used for judging whether the current crowdsourcing state meets the stopping condition or not according to the updated crowdsourcing state and an optimal time stopping rule, stopping the high-precision map crowdsourcing task if the stopping condition is met, and continuing to enter the next time slice if the stopping condition is not met.
The invention also provides a data collection method of the high-precision map crowdsourcing system based on the optimal time-stop rule, which comprises the following steps:
s1, firstly, a task issuing module acquires historical road network data, wherein the historical road network data refers to historical traffic flow data, the current traffic flow is predicted through a grey prediction classical model GM (1,1), then time is discretized into a time slice form, the time slice length and the longest execution time of a high-precision map crowdsourcing task are defined according to the traffic flow, and finally the high-precision map crowdsourcing task is issued by giving a reward according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected number of participants in a unit time slice;
s2, the member optimization module firstly obtains the vehicle-mounted sensor equipment information and the credit value of the candidate participants in the current time slice, calculates the score of each candidate participant, then selects the winning participant according to the score of the candidate participants in the current time slice and the expected number of the participants in the unit time slice, receives the data collected by the winning participant, and finally provides the reward for the winning participant;
and S3, the optimal time stopping module firstly updates the current accumulated collected data volume, updates the crowdsourcing state according to the current accumulated collected data volume, then judges whether the current crowdsourcing state meets the stopping condition according to the optimal time stopping rule, stops the high-precision map crowdsourcing task if the stopping condition is met, and otherwise continues to enter the next time slice.
Further, the step S1 includes the steps of:
s101, obtaining historical road network data, and predicting the current traffic flow lambda through a grey prediction classical model GM (1, 1);
s102, discretizing time into a time slice form, and defining time slice length T based on the predicted current traffic flow lambdaWComprises the following steps:
Figure BDA0002915904130000041
in the formula, n0Selecting the number of participants expected for a unit time slice; defining best-effort crowd-sourced tasks for high-precision mapsThe long execution time T is:
Figure BDA0002915904130000042
in the formula, T0Crowd-sourcing a reference value, lambda, of the longest execution time of a task for a high-precision map predefined by the system0Predefined correspondence T for system0A vehicle flow reference value of;
s103, according to the time slice length TWThe longest execution time T of the high-precision map crowdsourcing task and the expected selection number n of participants in unit time slice0And additionally defining reward b to issue a high-precision map crowdsourcing task, wherein the format of the sent message is < T, TW,n0B > represents that the longest execution time of the high-precision map crowdsourcing task is T, and T is T by lengthWEach slot is expected to receive n0Data for each participant, each winning participant will receive a reward b.
Further, the step S2 includes the steps of:
s201, obtaining vehicle-mounted sensor equipment information and credit values of candidate participants, wherein the vehicle-mounted sensor equipment information comprises the quality and the quantity of cameras and the quality and the quantity of laser radars, and calculating the score of each candidate participant according to the quality and the quantity, and the calculation formula of the score is as follows:
Figure BDA0002915904130000043
where s is the score of the candidate participant, r is the reputation value of the candidate participant representing the trustworthiness of the data provided by the candidate participant, and ncIs the number of the cameras and is,
Figure BDA0002915904130000051
is the mass of the ith camera, nlIn order to be able to count the number of lidar,
Figure BDA0002915904130000052
is as followsThe quality of i lidar; in addition, the quality of the camera is defined as follows:
Figure BDA0002915904130000053
in the formula (I), the compound is shown in the specification,
Figure BDA0002915904130000054
the pixel value of the ith camera is; the quality of the lidar is defined as follows:
Figure BDA0002915904130000055
in the formula (I), the compound is shown in the specification,
Figure BDA0002915904130000056
the number of lines of the ith laser radar is;
s202, selecting the number n of participants in a unit time slice according to the scores of the candidate participants in the current time slice and the expected selection number of the participants in the unit time slice0To select a winning participant, which is the top n with the highest score among the candidate participants in the current time slice0A bit; then, receiving data collected by the winning participant;
and S203, providing a reward b for each winning participant.
Further, the step S3 includes the steps of:
s301, updating the current accumulated collected data volume, wherein the updating formula of the current accumulated collected data volume is as follows:
Figure BDA0002915904130000057
wherein n represents a time slice number, D (n) and D (n-1) represent the data amount accumulated and collected until the nth time and the data amount accumulated and collected until the n-1 th time, respectively, and D (n) represents the data amount collected during the nth time slice; updating the crowdsourcing state according to the current accumulated collected data volume, wherein the crowdsourcing state is defined as:
Figure BDA0002915904130000061
wherein Y (n) is the crowdsourcing state of the nth time slice, e is the base number of the natural logarithm, and theta is the system coefficient;
s302, defining an optimal time-stop rule as follows:
n*＝min{n:1≤n≤M,Y(n)≥δ,ΔY(n)≤Φ}
in the formula, n*The optimal stopping time of the high-precision map crowdsourcing task is given by an optimal stopping rule, M is the number of time slices contained in the high-precision map crowdsourcing task, delta is a minimum stopping threshold value of the crowdsourcing state of the high-precision map crowdsourcing task, delta Y (n) is an expected value of the crowdsourcing state gain of the next time slice under the condition that the crowdsourcing state of the nth time slice is known, phi is the cost generated by the high-precision map crowdsourcing task in a unit time slice, and
Figure BDA0002915904130000062
in the formula, n0The number of participants to be selected for a unit time slice,
Figure BDA0002915904130000063
an average of the amount of data provided for the winning participant, b a reward provided for the winning participant; if the current time slice meets the optimal stopping rule, stopping the crowdsourcing task of the high-precision map; otherwise, continuing the crowdsourcing task of the high-precision map, and entering the next time slice.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention collects road surface environment data required by updating the high-precision map by a crowdsourcing mechanism, and utilizes social vehicles to collect data, thereby solving the problem of small quantity of professional collection vehicles, greatly improving the efficiency of data collection, and having much less cost than the collection of data by the collection vehicles.
2. According to the invention, the score of each candidate participant is calculated through the vehicle-mounted sensor equipment information and the credit value of the candidate participant, and the winning participant is screened out based on the score, so that the quality of the collected road environment data is ensured, and the reliability of the collected road environment data is also ensured.
3. According to the method, the stopping time of the high-precision map crowdsourcing task is judged according to the optimal time stopping rule, and the benefit of the high-precision map crowdsourcing task is maximized on the premise that the data volume of the collected road surface environment data meets the requirement of the high-precision map crowdsourcing task.
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FIG. 1 is a block diagram of the various modules of the system of the present invention.
FIG. 2 is a diagram illustrating the calling process of each module of the system according to the present invention.
FIG. 3 is a schematic flow chart of the method of the present invention.
FIG. 4 is a schematic diagram of the operation of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples.
The high-precision map crowdsourcing system based on the optimal time-stop rule provided by the embodiment is a data collection system for updating a high-precision map, and fig. 1 and 2 show modules included in the system and calling processes among the modules respectively. It comprises the following components:
the task issuing module is used for issuing a high-precision map crowdsourcing task;
the member optimization module is used for realizing selection of a winning participant in the candidate participant set, receiving data of the winning participant and providing consideration for the winning participant;
and the optimal time stopping module is used for controlling the stopping time of the high-precision map crowdsourcing task so as to maximize crowdsourcing utility.
The task issuing module comprises a traffic flow prediction module, a time division module and an issuing module, wherein:
the traffic flow prediction module is responsible for predicting the current traffic flow through the historical data of the road network;
the time division module is responsible for dispersing time into a time slice form, and calculating the length of the time slice and the longest execution time of a high-precision map crowdsourcing task according to a traffic flow prediction result;
the issuing module is responsible for issuing the high-precision map crowdsourcing task according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected selection number of participants in a unit time slice, and in addition, a reward is given.
The member preference module comprises a scoring module, a selection module and a reward module, wherein:
the score module is responsible for calculating the score of each candidate participant according to the vehicle-mounted sensor equipment information and the reputation value of the candidate participant in the current time slice;
the selection module is responsible for selecting a winning participant according to the score of each candidate participant in the current time slice and receiving data collected by the winning participant;
the reward module is responsible for providing a reward to the winning participant.
The optimal time-out module comprises a crowdsourcing state updating module and a stop decision module, wherein:
the crowdsourcing state updating module is responsible for updating the current accumulated collected data volume and updating the crowdsourcing state according to the current accumulated collected data volume;
and the stopping decision module is used for judging whether the current crowdsourcing state meets the stopping condition or not according to the updated crowdsourcing state and an optimal time stopping rule, stopping the high-precision map crowdsourcing task if the stopping condition is met, and continuing to enter the next time slice if the stopping condition is not met.
As shown in fig. 3, the present embodiment also provides the data collection method of the above-mentioned high-precision map crowdsourcing system based on the optimal stop-and-go rule, including the following steps:
s1, firstly, a task issuing module acquires historical road network data, wherein the historical road network data refers to historical traffic flow data, the current traffic flow is predicted through a grey prediction classical model GM (1,1), then time is discretized into a time slice form, the time slice length and the longest execution time of a high-precision map crowdsourcing task are defined according to the traffic flow, finally, the high-precision map crowdsourcing task is issued according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected selection number of participants in a unit time slice, and further, a reward is given, and the specific process is as follows:
s101, obtaining historical road network data, and predicting the current traffic flow lambda through a grey prediction classical model GM (1, 1);
s102, discretizing time into a time slice form, and defining time slice length T based on the predicted current traffic flow lambdaWComprises the following steps:
Figure BDA0002915904130000091
in the formula, n0Selecting the number of participants expected for a unit time slice; defining the longest execution time T of the high-precision map crowdsourcing task as follows:
Figure BDA0002915904130000092
in the formula, T0Crowd-sourcing a reference value, lambda, of the longest execution time of a task for a high-precision map predefined by the system0Predefined correspondence T for system0A vehicle flow reference value of;
s103, according to the time slice length TWThe longest execution time T of the high-precision map crowdsourcing task and the expected selection number n of participants in unit time slice0And additionally defining reward b to issue a high-precision map crowdsourcing task, wherein the format of the sent message is < T, TW,n0B > represents that the longest execution time of the high-precision map crowdsourcing task is T, and T is T by lengthWEach slot is expected to receive n0Data for each participant, each winning participant will receive a reward b.
S2, the member optimization module firstly obtains the vehicle-mounted sensor equipment information and the credit value of the candidate participants in the current time slice, calculates the score of each candidate participant, then selects the winning participant according to the score of the candidate participants in the current time slice and the expected number of the participants in the unit time slice, receives the data collected by the winning participant, and finally provides the reward for the winning participant, the concrete process is as follows:
s201, obtaining vehicle-mounted sensor equipment information and credit values of candidate participants, wherein the vehicle-mounted sensor equipment information comprises the quality and the quantity of cameras and the quality and the quantity of laser radars, and calculating the score of each candidate participant according to the quality and the quantity, and the calculation formula of the score is as follows:
Figure BDA0002915904130000101
where s is the score of the candidate participant, r is the reputation value of the candidate participant representing the trustworthiness of the data provided by the candidate participant, and ncIs the number of the cameras and is,
Figure BDA0002915904130000102
is the mass of the ith camera, nlIn order to be able to count the number of lidar,
Figure BDA0002915904130000103
the quality of the ith laser radar; in addition, the quality of the camera is defined as follows:
Figure BDA0002915904130000104
in the formula (I), the compound is shown in the specification,
Figure BDA0002915904130000105
the pixel value of the ith camera is; the quality of the lidar is defined as follows:
Figure BDA0002915904130000106
in the formula (I), the compound is shown in the specification,
Figure BDA0002915904130000107
the number of lines of the ith laser radar is;
s202, selecting the number n of participants in a unit time slice according to the scores of the candidate participants in the current time slice and the expected selection number of the participants in the unit time slice0To select a winning participant, which is the top n with the highest score among the candidate participants in the current time slice0A bit; then, receiving data collected by the winning participant;
and S203, providing a reward b for each winning participant.
S3, the optimal time stopping module firstly updates the current accumulated collected data volume, updates the crowdsourcing state according to the current accumulated collected data volume, then judges whether the current crowdsourcing state meets the stopping condition according to the optimal time stopping rule, if the stopping condition is met, the high-precision map crowdsourcing task is stopped, otherwise, the next time slice is continuously entered, and the specific process is as follows:
s301, updating the current accumulated collected data volume, wherein the updating formula of the current accumulated collected data volume is as follows:
Figure BDA0002915904130000111
wherein n represents a time slice number, D (n) and D (n-1) represent the data amount accumulated and collected until the nth time and the data amount accumulated and collected until the n-1 th time, respectively, and D (n) represents the data amount collected during the nth time slice; updating the crowdsourcing state according to the current accumulated collected data volume, wherein the crowdsourcing state is defined as:
Figure BDA0002915904130000112
wherein Y (n) is the crowdsourcing state of the nth time slice, e is the base number of the natural logarithm, and theta is the system coefficient;
s302, defining an optimal time-stop rule as follows:
n*＝min{n:1≤n≤M,Y(n)≥δ,ΔY(n)≤Φ}
in the formula, n*The optimal stopping time of the high-precision map crowdsourcing task is given by an optimal stopping rule, M is the number of time slices contained in the high-precision map crowdsourcing task, delta is a minimum stopping threshold value of the crowdsourcing state of the high-precision map crowdsourcing task, delta Y (n) is an expected value of the crowdsourcing state gain of the next time slice under the condition that the crowdsourcing state of the nth time slice is known, phi is the cost generated by the high-precision map crowdsourcing task in a unit time slice, and
Figure BDA0002915904130000113
in the formula, n0The number of participants to be selected for a unit time slice,
Figure BDA0002915904130000114
an average of the amount of data provided for the winning participant, b a reward provided for the winning participant; if the current time slice meets the optimal stopping rule, stopping the crowdsourcing task of the high-precision map; otherwise, continuing the crowdsourcing task of the high-precision map, and entering the next time slice.
As shown in fig. 4, a data collection operation flow of the high-precision map crowdsourcing system of the embodiment is specifically as follows:
step 1: obtaining road network historical data, wherein the road network historical data refers to historical traffic flow data, and predicting the current traffic flow through a grey prediction classical model GM (1, 1);
step 2: defining the length of a time slice and the longest execution time of a high-precision map crowdsourcing task based on the current traffic flow, wherein the time slice is a discretization expression form of time;
and step 3: combining the time slice length and the longest execution time of the high-precision map crowdsourcing task, additionally defining the expected selection number and reward of participants in unit time slices, and issuing the high-precision map crowdsourcing task by using the four elements;
and 4, step 4: entering a time slice, and calculating the scores of candidate participants in the current time slice;
and 5: selecting candidate participants with highest scores as winning participants in sequence by taking the scores as a judgment standard, taking the selection number of the winning participants as the expected selection number of the participants in a unit time slice, and then receiving data collected by the winning participants;
step 6: providing a reward for the winning participant;
and 7: updating the current accumulated collected data volume and the crowdsourcing state according to the data volume collected by the current time slice;
and 8: judging whether the current crowdsourcing state meets a stopping condition or not according to an optimal stopping rule, and if so, ending the high-precision map crowdsourcing task; and otherwise, entering the next time slice and continuing to perform the crowdsourcing task of the high-precision map.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims (8)

1. High-precision map crowdsourcing system based on optimal time-stop rules is characterized by comprising the following steps:
the task issuing module is used for issuing a high-precision map crowdsourcing task;
the member optimization module is used for realizing selection of a winning participant in the candidate participant set, receiving data of the winning participant and providing consideration for the winning participant;
and the optimal time stopping module is used for controlling the stopping time of the high-precision map crowdsourcing task so as to maximize crowdsourcing utility.
2. The optimal stop-and-go rule-based high precision map crowd-sourcing system of claim 1, wherein: the task issuing module comprises a traffic flow prediction module, a time division module and an issuing module, wherein:
the traffic flow prediction module is responsible for predicting the current traffic flow through the historical data of the road network;
the time division module is responsible for dispersing time into a time slice form, and calculating the length of the time slice and the longest execution time of a high-precision map crowdsourcing task according to a traffic flow prediction result;
the issuing module is responsible for issuing the high-precision map crowdsourcing task according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected selection number of participants in a unit time slice, and in addition, a reward is given.
3. The optimal stop-and-go rule-based high precision map crowd-sourcing system of claim 1, wherein: the member preference module comprises a scoring module, a selection module and a reward module, wherein:
the score module is responsible for calculating the score of each candidate participant according to the vehicle-mounted sensor equipment information and the reputation value of the candidate participant in the current time slice;
the selection module is responsible for selecting a winning participant according to the score of each candidate participant in the current time slice and receiving data collected by the winning participant;
the reward module is responsible for providing a reward to the winning participant.
4. The optimal stop-and-go rule-based high precision map crowd-sourcing system of claim 1, wherein: the optimal time-out module comprises a crowdsourcing state updating module and a stop decision module, wherein:
the crowdsourcing state updating module is responsible for updating the current accumulated collected data volume and updating the crowdsourcing state according to the current accumulated collected data volume;
and the stopping decision module is used for judging whether the current crowdsourcing state meets the stopping condition or not according to the updated crowdsourcing state and an optimal time stopping rule, stopping the high-precision map crowdsourcing task if the stopping condition is met, and continuing to enter the next time slice if the stopping condition is not met.
5. The data collection method of the high-precision map crowdsourcing system based on the optimal time-stopping rule is characterized by comprising the following steps:
s1, firstly, a task issuing module acquires historical road network data, wherein the historical road network data refers to historical traffic flow data, the current traffic flow is predicted through a grey prediction classical model GM (1,1), then time is discretized into a time slice form, the time slice length and the longest execution time of a high-precision map crowdsourcing task are defined according to the traffic flow, and finally the high-precision map crowdsourcing task is issued by giving a reward according to the time slice length, the longest execution time of the high-precision map crowdsourcing task and the expected number of participants in a unit time slice;
s2, the member optimization module firstly obtains the vehicle-mounted sensor equipment information and the credit value of the candidate participants in the current time slice, calculates the score of each candidate participant, then selects the winning participant according to the score of the candidate participants in the current time slice and the expected number of the participants in the unit time slice, receives the data collected by the winning participant, and finally provides the reward for the winning participant;
and S3, the optimal time stopping module firstly updates the current accumulated collected data volume, updates the crowdsourcing state according to the current accumulated collected data volume, then judges whether the current crowdsourcing state meets the stopping condition according to the optimal time stopping rule, stops the high-precision map crowdsourcing task if the stopping condition is met, and otherwise continues to enter the next time slice.
6. The data collection method of the optimal stop-and-go rule-based high-precision map crowdsourcing system according to claim 5, wherein the step S1 comprises the following steps:
s101, obtaining historical road network data, and predicting the current traffic flow lambda through a grey prediction classical model GM (1, 1);
s102, discretizing time into a time slice form, and defining time slice length T based on the predicted current traffic flow lambdaWComprises the following steps:
Figure FDA0002915904120000031
in the formula, n0Selecting the number of participants expected for a unit time slice; defining the longest execution time T of the high-precision map crowdsourcing task as follows:
Figure FDA0002915904120000032
in the formula, T0Crowd-sourcing a reference value, lambda, of the longest execution time of a task for a high-precision map predefined by the system0Predefined correspondence T for system0A vehicle flow reference value of;
s103, according to the time slice length TWThe longest execution time T of the high-precision map crowdsourcing task and the expected selection number n of participants in unit time slice0And additionally defining reward b to issue a high-precision map crowdsourcing task, wherein the format of the sent message is < T, TW,n0B > represents that the longest execution time of the high-precision map crowdsourcing task is T, and T is T by lengthWEach slot is expected to receive n0Data for each participant, each winning participant will receive a reward b.
7. The data collection method of the optimal stop-and-go rule-based high-precision map crowdsourcing system according to claim 5, wherein the step S2 comprises the following steps:
s201, obtaining vehicle-mounted sensor equipment information and credit values of candidate participants, wherein the vehicle-mounted sensor equipment information comprises the quality and the quantity of cameras and the quality and the quantity of laser radars, and calculating the score of each candidate participant according to the quality and the quantity, and the calculation formula of the score is as follows:
Figure FDA0002915904120000041
in the formula, s isThe score of the candidate participant, r is the reputation value of the candidate participant, representing the trustworthiness of the data provided by the candidate participant, ncIs the number of the cameras and is,
Figure FDA0002915904120000042
is the mass of the ith camera, nlIn order to be able to count the number of lidar,
Figure FDA0002915904120000043
the quality of the ith laser radar; in addition, the quality of the camera is defined as follows:
Figure FDA0002915904120000044
in the formula (I), the compound is shown in the specification,
Figure FDA0002915904120000045
the pixel value of the ith camera is; the quality of the lidar is defined as follows:
Figure FDA0002915904120000046
in the formula (I), the compound is shown in the specification,
Figure FDA0002915904120000047
the number of lines of the ith laser radar is;
s202, selecting the number n of participants in a unit time slice according to the scores of the candidate participants in the current time slice and the expected selection number of the participants in the unit time slice0To select a winning participant, which is the top n with the highest score among the candidate participants in the current time slice0A bit; then, receiving data collected by the winning participant;
and S203, providing a reward b for each winning participant.
8. The data collection method of the optimal stop-and-go rule-based high-precision map crowdsourcing system according to claim 5, wherein the step S3 comprises the following steps:
s301, updating the current accumulated collected data volume, wherein the updating formula of the current accumulated collected data volume is as follows:
Figure FDA0002915904120000048
wherein n represents a time slice number, D (n) and D (n-1) represent the data amount accumulated and collected until the nth time and the data amount accumulated and collected until the n-1 th time, respectively, and D (n) represents the data amount collected during the nth time slice; updating the crowdsourcing state according to the current accumulated collected data volume, wherein the crowdsourcing state is defined as:
Figure FDA0002915904120000051
wherein Y (n) is the crowdsourcing state of the nth time slice, e is the base number of the natural logarithm, and theta is the system coefficient;
s302, defining an optimal time-stop rule as follows:
n*＝min{n:1≤n≤M,Y(n)≥δ,ΔY(n)≤Φ}
in the formula, n*The optimal stopping time of the high-precision map crowdsourcing task is given by an optimal stopping rule, M is the number of time slices contained in the high-precision map crowdsourcing task, delta is a minimum stopping threshold value of the crowdsourcing state of the high-precision map crowdsourcing task, delta Y (n) is an expected value of the crowdsourcing state gain of the next time slice under the condition that the crowdsourcing state of the nth time slice is known, phi is the cost generated by the high-precision map crowdsourcing task in a unit time slice, and
Figure FDA0002915904120000052
in the formula, n0The number of participants to be selected for a unit time slice,
Figure FDA0002915904120000053
an average of the amount of data provided for the winning participant, b a reward provided for the winning participant; if the current time slice meets the optimal stopping rule, stopping the crowdsourcing task of the high-precision map; otherwise, continuing the crowdsourcing task of the high-precision map, and entering the next time slice.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113503869A (en) * 2021-06-25 2021-10-15 华南理工大学 High-precision map crowdsourcing vehicle selection method based on regional perception

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140278634A1 (en) * 2013-03-15 2014-09-18 Microsoft Corporation Spatiotemporal Crowdsourcing
CN107103408A (en) * 2017-03-24 2017-08-29 扬州大学 Complex task distribution method under a kind of mass-rent environment
CN107784561A (en) * 2017-11-10 2018-03-09 东南大学 The implementation method of online incentive mechanism in a kind of mobile mass-rent system
CN108364125A (en) * 2018-01-30 2018-08-03 华南理工大学 A kind of vehicle moving crowdsourcing multiple target task distribution method
US20180260902A1 (en) * 2017-03-11 2018-09-13 Michael John Curran System and method for determining an optimal stopping time
CN109902903A (en) * 2017-12-11 2019-06-18 华东师范大学 A kind of crowdsourcing execution method and crowdsourcing system based on wechat
CN110110962A (en) * 2019-04-02 2019-08-09 华南理工大学 A kind of task gunz executes the preferred method of team
CN110160544A (en) * 2019-06-12 2019-08-23 北京深思敏行科技有限责任公司 A kind of high-precision map crowdsourcing more new system based on edge calculations
CN111428932A (en) * 2020-03-27 2020-07-17 中国民航大学 Medium-and-long-term air traffic flow prediction method based on wavelet transformation and gray prediction
CN111915185A (en) * 2020-07-31 2020-11-10 湖北大学 Space-time crowdsourcing task allocation method and device based on path planning strategy

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140278634A1 (en) * 2013-03-15 2014-09-18 Microsoft Corporation Spatiotemporal Crowdsourcing
US20180260902A1 (en) * 2017-03-11 2018-09-13 Michael John Curran System and method for determining an optimal stopping time
CN107103408A (en) * 2017-03-24 2017-08-29 扬州大学 Complex task distribution method under a kind of mass-rent environment
CN107784561A (en) * 2017-11-10 2018-03-09 东南大学 The implementation method of online incentive mechanism in a kind of mobile mass-rent system
CN109902903A (en) * 2017-12-11 2019-06-18 华东师范大学 A kind of crowdsourcing execution method and crowdsourcing system based on wechat
CN108364125A (en) * 2018-01-30 2018-08-03 华南理工大学 A kind of vehicle moving crowdsourcing multiple target task distribution method
CN110110962A (en) * 2019-04-02 2019-08-09 华南理工大学 A kind of task gunz executes the preferred method of team
CN110160544A (en) * 2019-06-12 2019-08-23 北京深思敏行科技有限责任公司 A kind of high-precision map crowdsourcing more new system based on edge calculations
CN111428932A (en) * 2020-03-27 2020-07-17 中国民航大学 Medium-and-long-term air traffic flow prediction method based on wavelet transformation and gray prediction
CN111915185A (en) * 2020-07-31 2020-11-10 湖北大学 Space-time crowdsourcing task allocation method and device based on path planning strategy

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113503869A (en) * 2021-06-25 2021-10-15 华南理工大学 High-precision map crowdsourcing vehicle selection method based on regional perception
CN113503869B (en) * 2021-06-25 2023-06-20 华南理工大学 High-precision map crowdsourcing vehicle selection method based on regional perceptibility

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