WO2017171429A2 - Système d'estimation de consommation de carburant sur la base d'une grande analyse de données spatiale - Google Patents

Système d'estimation de consommation de carburant sur la base d'une grande analyse de données spatiale Download PDF

Info

Publication number
WO2017171429A2
WO2017171429A2 PCT/KR2017/003485 KR2017003485W WO2017171429A2 WO 2017171429 A2 WO2017171429 A2 WO 2017171429A2 KR 2017003485 W KR2017003485 W KR 2017003485W WO 2017171429 A2 WO2017171429 A2 WO 2017171429A2
Authority
WO
WIPO (PCT)
Prior art keywords
data
road
fuel consumption
driving
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2017/003485
Other languages
English (en)
Korean (ko)
Other versions
WO2017171429A3 (fr
Inventor
최은미
조원희
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kookmin University
Original Assignee
Kookmin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020170040080A external-priority patent/KR101932695B1/ko
Application filed by Kookmin University filed Critical Kookmin University
Publication of WO2017171429A2 publication Critical patent/WO2017171429A2/fr
Publication of WO2017171429A3 publication Critical patent/WO2017171429A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Definitions

  • the technical idea of the present invention relates to a fuel consumption estimation system, and more particularly, to a fuel consumption estimation system based on spatial big data analysis.
  • the existing techniques use the average speed-driven variable, which does not reflect the various driving patterns of the driver during actual driving inherent in the driving record.
  • existing techniques have difficulty in reflecting information such as road inclination or road type in a real environment which affects fuel consumption.
  • Korean Patent No. 10-1526431 discloses a model for estimating fuel efficiency for any vehicle by receiving information related to various driving from a plurality of actual driving vehicles.
  • Korean Patent No. 10-1526431 does not reflect spatial information such as the situation of a road on which a plurality of vehicles are actually driven, so there is a problem in that it is not possible to accurately estimate fuel consumption.
  • the technical problem of the present invention is to minimize the error while using the driving data of the actual driving vehicle, fuel consumption that can accurately estimate the fuel consumption of the vehicle by reflecting various driving pattern information and spatial information To provide an estimation system.
  • a preprocessing unit is configured to refine driving data obtained from a vehicle;
  • a first processor obtaining road attribute information corresponding to a driving route of the vehicle based on the refined driving data and map data, and reflecting the road attribute information to the purified driving data;
  • a second processor configured to generate statistical data based on the driving data reflecting the road property information;
  • a fuel consumption estimating unit estimating fuel consumption of the vehicle based on at least one of driving data and the statistical data reflecting the road property information.
  • the first processing unit may match the GPS coordinates representing the driving route of the vehicle in the refined driving data with a map represented by the map data, and then convert a road attribute for the GPS coordinates from the map data.
  • a road property information reflecting unit obtaining information and reflecting the road property information to the refined driving data.
  • the road attribute information reflecting unit selects a matching link for the GPS coordinates based on the position of the GPS coordinates on the map, and the ID of the selected matching link is a link of the GPS coordinates.
  • the road property information may be allocated to an ID, and the road property information may be obtained from the map data based on the assigned link ID.
  • the road attribute information reflecting unit converts the GPS coordinates into a spatial index corresponding to the map data, and m (where m is a natural number) GPS coordinates among the converted spatial indexes.
  • the candidate links adjacent to the m th GPS coordinates are selected on the map using a spatial index corresponding to, and the distance weights for each of the candidate links are determined based on the distance between the m th GPS coordinates and the candidate links.
  • Road attribute information for the m th GPS coordinate may be obtained from the data.
  • the road attribute information reflecting unit may further add a history weight for each of the candidate links based on the number of times the candidate links have been previously assigned as link IDs for GPS coordinates other than the mth GPS coordinates.
  • the matching link may be selected from the candidate links based on the calculated history weight and the calculated distance weight.
  • the road attribute information reflector further calculates a speed weight of each of the candidate links by comparing the speed of the vehicle and the road speed limit on the candidate links at the mth GPS coordinates.
  • the matching link may be selected from the candidate links based on the calculated speed weights and the calculated distance weights.
  • the road property information reflecting unit may calculate a distance between m + 1th GPS coordinates and the mth GPS coordinates, determine whether the calculated distance meets a preset criterion, and If the calculated distance does not meet the preset criterion, the ID of the link allocated for the mth GPS coordinates is assigned as the link ID of the m + 1th GPS coordinates, and the map is based on the assigned link ID.
  • Road attribute information for the m + 1 th GPS coordinates may be obtained from the data.
  • the first processing unit obtains the inclination information corresponding to the driving route of the vehicle based on the refined driving data and the altitude data, and reflects the inclination information in the refined driving data.
  • the second processor may generate the statistical data based on driving data in which the slope information and the road property information are reflected.
  • the elevation data may include at least one of digital elevation model (DEM) data, GPS elevation data, and road gradient data.
  • DEM digital elevation model
  • the first processing unit based on the GPS coordinates indicating the driving route of the vehicle in the refined driving data and the altitude data corresponding to each of the GPS coordinates among the altitude data, the GPS coordinates. And an inclination information reflector for acquiring inclination information in the field and reflecting the inclination information in the refined driving data.
  • the inclination information reflector calculates a distance between an nth (where n is a natural number) GPS coordinate and an n + 1th GPS coordinate, and the calculated distance meets a preset criterion. If the calculated distance meets the preset criteria, based on the calculated distance, the altitude data corresponding to the n-th GPS coordinates and the altitude data corresponding to the n + 1th GPS coordinates; The slope at the n + 1 th GPS coordinate may be calculated.
  • the inclination information reflector calculates a distance between an nth (where n is a natural number) GPS coordinate and an n + 1th GPS coordinate, and the calculated distance meets a preset criterion. If it is determined whether the calculated distance does not meet the preset criterion, the inclination at the n + 1th GPS coordinates may be treated as 0.
  • the second processing unit may generate the statistical data by statistically analyzing driving data in which the road property information is reflected.
  • the driving data reflecting the road attribute information may include driving distance, driving time, data acquisition period, data acquisition date and time, speed, engine revolutions per minute, brake signal, position, azimuth, acceleration, and MAP (Manifold). Records for at least one of Absolute Pressure (MAF), Mass Air Flow (MAF), Fuel Ejection, Road Name, Road Type, Road Facility Type, Number of Lanes, Road Width, Road Speed Limit and Toll Road,
  • the statistical data includes average speed, average engine revolutions per minute (RPM), average stop time, number of stops, speed standard deviation, RPM standard deviation, speed increase standard deviation, speed reduction standard deviation, speed and RPM correlation coefficient, vehicle speed And GPS conversion speed difference, the number of speeding, the number of dangerous speeds, the number of rapid accelerations, the number of sudden decelerations, the number of sudden starts, the number of sudden stops, idlings, the speed section ratio, the RPM section ratio, fuel consumption, fuel balance And may include a record for the at least one field of the fuel, carbon dioxide emissions, and
  • the fuel consumption estimation unit the fuel consumption estimation model through a supervised learning analysis technique using at least one of the field of the driving data and the statistical data field reflecting the road property information as a variable
  • estimate a fuel consumption of the vehicle by applying a record of at least one of the fields of the driving data and the statistical data in which the road property information is reflected to the generated fuel consumption estimation model.
  • an error may be minimized while using driving data of an actual driving vehicle, and fuel consumption of the vehicle may be accurately estimated by reflecting various driving pattern information and spatial information.
  • FIG. 1 is a block diagram conceptually illustrating a part of a configuration of a fuel consumption estimation system according to an embodiment of the inventive concept.
  • FIG. 2 is a flowchart illustrating a fuel consumption estimation process according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating an example of step S210 of FIG. 2.
  • FIG. 4 is a diagram illustrating an example of step S230 of FIG. 2.
  • FIG. 5 is a diagram illustrating an example of step S231 of FIG. 4, and FIG. 6 is a diagram for describing a gradient calculation process.
  • FIG. 7 is a diagram illustrating an example of step S233 of FIG. 4, and FIG. 8 is a diagram for describing a road attribute and a map matching result.
  • FIG. 9 is a diagram illustrating an example of step S250 of FIG. 2, and FIGS. 10 and 11 are diagrams for explaining data related to statistical analysis.
  • FIG. 12 is a diagram illustrating an example of step S270 of FIG. 2.
  • FIG. 13 is a view schematically illustrating an environment in which a fuel consumption estimation system is used, according to an embodiment of the inventive concept.
  • one component when one component is referred to as “connected” or “connected” with another component, the one component may be directly connected or directly connected to the other component, but in particular It is to be understood that, unless there is an opposite substrate, it may be connected or connected via another component in the middle.
  • ⁇ part refers to a unit for processing at least one function or operation, which is a processor, a micro Processor (Micro Processor), Micro Controller, Central Processing Unit (CPU), Graphics Processing Unit (GPU), Accelerate Processor Unit (APU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), FPGA It may be implemented by hardware or software such as (Field Programmable Gate Array) or a combination of hardware and software.
  • Micro Processor Micro Processor
  • CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • APU Accelerate Processor Unit
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FIG. 1 is a block diagram conceptually illustrating a part of a configuration of a fuel consumption estimation system according to an embodiment of the inventive concept.
  • the fuel consumption estimating system 100 includes a preprocessor 110, a first processor 120, a second processor 130, a fuel consumption estimator 140, a user interface 150, and a database. 160 may be included.
  • the preprocessor 110 may refine data related to driving (for example, information about a vehicle's speed, acceleration, RPM, GPS coordinates, etc.) (hereinafter, referred to as driving data) obtained from a plurality of vehicles according to a preset method. refine).
  • driving data may be data acquired by a DTG terminal, an on-board diagnostic (OBD) -II terminal, or the like mounted on each of the plurality of vehicles.
  • OBD on-board diagnostic
  • the driving data may be data obtained from various devices for sensing information related to a moving path of vehicles.
  • the driving data may be directly transmitted from the plurality of vehicles to the fuel consumption estimation system 100, but is not limited thereto.
  • the preprocessing unit 110 performs format conversion processing, data division processing, and the like so as to be suitable for data processing and analysis in the first processing unit 120 or the like, which is input from an external device (not shown) or stored in advance. Can be done.
  • the first processing unit 120 may obtain information on the inclination corresponding to the driving route of the vehicle (hereinafter, referred to as inclination information) based on the driving data and the altitude data purified by the preprocessor 110.
  • the first processor 120 may acquire information on road attributes corresponding to the driving route of the vehicle (hereinafter referred to as road attribute information) based on the driving data and the map data purified by the preprocessor 110.
  • road attribute information may include information on a road name, information on a type of road (highway, national highway, etc.), lane number, road width information, road speed limit information, toll information, and the like. It may include.
  • the first processor 120 may reflect the inclination information and the road attribute information in the refined driving data. For example, the first processor 120 may generate at least one field and record for each of the slope information and the road attribute information, and add the generated field and record to the refined driving data. .
  • the first processor 120 may be configured of the gradient information reflector 121 and the road attribute information reflector 123.
  • the inclination information reflecting unit 121 is the inclination information in the GPS coordinates based on the GPS coordinates indicating the driving route of the vehicle in the refined driving data and the altitude-related values corresponding to each of the GPS coordinates among the altitude data. May be obtained, and the slope information may be reflected in the refined driving data.
  • the road property information reflecting unit 123 obtains road property information for the GPS coordinates from the map data by matching GPS coordinates representing the driving route of the vehicle with the map represented by the map data in the refined driving data, The obtained road property information may be reflected in the purified driving data.
  • the second processor 130 may generate statistical data according to a preset method based on driving data processed by the first processing unit 120, for example, driving data in which the slope information and the road property information are reflected. Can be.
  • the fuel consumption estimator 140 estimates fuel consumption of any vehicle based on at least one of driving data processed by the first processor 120 and statistical data generated by the second processor 130. Can be.
  • the user interface 150 may provide the user with an estimation result of the fuel consumption estimator 140, and, according to an embodiment, the user interface 150 may include a visualization processor.
  • the visualization processor visualizes the driving data, the statistical data, and the result data corresponding to the estimation result of the fuel consumption estimating unit 140 so that a user such as a driver, a transportation company, or the like may directly recognize the driving data.
  • the database 160 may store data processed and / or generated in each component of the fuel consumption estimation system 100, data input from an external device, and the like.
  • the fuel consumption estimation system 100 is a system based on big data processing technology such as Map Reduce technology and / or Apache SPARK using Hadoop ecosystem (HDFS, HBase, etc.) as an infrastructure for big data analysis. It may be configured as.
  • big data processing technology such as Map Reduce technology and / or Apache SPARK using Hadoop ecosystem (HDFS, HBase, etc.) as an infrastructure for big data analysis. It may be configured as.
  • HDFS Hadoop ecosystem
  • FIG. 2 is a flowchart illustrating a fuel consumption estimation process according to an embodiment of the present invention.
  • the fuel consumption estimation system 100 may perform driving data preprocessing to remove an outlier included in driving data obtained from a vehicle. Through this, the amount of data to be processed in the fuel consumption estimation system 100 may be reduced, and the accuracy of the estimated fuel consumption may be increased.
  • the fuel consumption estimating system 100 may perform predetermined preprocessing such as format conversion on altitude data and map data.
  • the fuel consumption estimating system 100 may obtain slope information and road attribute information based on the driving data, the altitude data, and the map data and reflect the same in the driving data.
  • the fuel consumption estimation system 100 may generate statistical data based on the driving data in which the slope information and the road property information are reflected.
  • the fuel consumption estimation system 100 may estimate fuel consumption based on at least one of the driving data and the statistical data.
  • the preprocessor 110 refines data about various types of driving included in driving data received from a plurality of arbitrary vehicles, for example, DTG data, OBD-II data, and the like. can do.
  • the preprocessing unit 110 may perform processing such as format conversion and data segmentation processing on the altitude data, map data, and the like so as to be suitable for the processing and analysis of the fuel consumption estimation system 110.
  • the preprocessing unit 110 may detect an outlier from the driving data (S211), and may purify the driving data by removing or correcting a record detected as the outlier (S213).
  • the preprocessor 110 may detect an outlier by determining whether a record of at least one data field included in the driving data meets a preset criterion (S2111), and detects an outlier. Records can be removed or corrected to meet the above criteria.
  • the preprocessor 110 may detect an outlier by deviation comparing consecutive records of at least one field with respect to the driving data (S2113), and may remove the record detected as the outlier.
  • the preprocessing unit 110 may compare the records of the fields correlated with each other with respect to the driving data and detect an outlier (S2115), and may remove the record detected as the outlier.
  • the preprocessing unit 110 may statistically analyze records of at least one field with respect to the driving data to detect records indicating abnormal driving patterns as outliers (S2117) and to remove records detected as outliers. Can be.
  • the preprocessing unit 110 may perform an operation of filtering and purifying driving data received from the vehicle, thereby increasing accuracy in data processing and analysis steps to be described later.
  • the preprocessing unit 110 may purify the driving data by six kinds of methods as illustrated in Table 1 below, and in relation to the purification of the driving data of the preprocessor 110 illustrated in Table 1 below, Exemplary examples disclosed in Korean Patent Nos. 10-1601031, 10-1601034, registered on March 2, and US Patent 9,600,541, registered on March 21, 2017, are incorporated herein by reference.
  • Step S231 of FIG. 4 may be performed by the slope information reflecting unit 121 of the first processing unit 120
  • step S233 of FIG. 3 may be performed by the road property information reflecting unit 123 of the first processing unit 120. Can be.
  • the gradient information reflecting unit 121 is n of GPS coordinates representing the driving route of the vehicle in the driving data purified by the preprocessor 110.
  • n is a natural number
  • a distance between the nth GPS coordinate and the n + 1th GPS coordinate may be calculated (S2311).
  • the slope information reflecting unit 121 may determine whether the calculated distance meets a preset criterion (S2313).
  • the criterion is used to determine whether the inclination calculation is unnecessary according to the distance difference between the n th GPS coordinate and the n + 1 th GPS coordinate, and may be set to an arbitrary value by the user.
  • the slope information reflecting unit 121 based on the calculated distance, altitude data corresponding to the nth GPS coordinates, and altitude data corresponding to the n + 1th GPS coordinates when the calculated distance meets a preset criterion.
  • the slope at the n + 1 th GPS coordinate may be calculated (S2315).
  • the tilt information reflecting unit 121 may process the tilt at the n + 1 th GPS coordinate as 0 (S2317).
  • the inclination information reflecting unit 121 may reflect the calculated inclination in the driving data purified by the preprocessor 110 as inclination information (S2319).
  • the driving data may include information on how much of the slope the associated vehicle has traveled.
  • the driving data obtained by the vehicle does not include information on road inclination, etc., when the fuel consumption of the vehicle is estimated using only the driving data of the vehicle, the accuracy is inevitably lowered.
  • the altitude data used when calculating the inclination of the inclination information reflecting unit 121 may be data including various types of altitude-related information.
  • the elevation data may be Digital Elevation Model (DEM) data. Since the DEM data is a data indicating the shape of the terrain by storing the altitude value of the terrain as a numerical value, it is apparent to those skilled in the art that the slope information reflecting unit 121 can perform the slope, slope direction, terrain analysis, etc. as the digital elevation model data itself. .
  • the DEM data may be stored in the GeoTiff format as a Raster image in the database 160.
  • the elevation data may be GPS elevation data.
  • the altitude data may include other types of data including information related to road inclination, such as road gradient data.
  • FIG. 6 (a) is a diagram illustrating the converted 30m interval DEM data on a contour map.
  • the method of calculating the degree of inclination (Degree) in the GPS trajectory by using the DEM data may vary, and detailed algorithms for calculating the degree of inclination may not be limited here because the scope of the technical idea of the present invention cannot be limited.
  • 6B is an example of expressing the calculated slope result on the contour map. You can see that the uphill part is displayed as + slope, and the downhill part is indicated as-slope.
  • Table 2 below is a table for explaining the case that the inclination information is used in estimating fuel consumption by the second processing unit 130 to be described later.
  • the inclination information may be reflected in the driving data purified by the preprocessor 110, and the driving data reflecting the inclination information may include vehicle number information, date information, time information, mileage information, ascent information, It may include downhill information.
  • the uphill information and / or the downhill information illustrated in Table 2 may be information on the sum of the slopes of the uphill and / or the sum of the slopes of the downhill for a unit time (for example, 5 minutes).
  • the driving data reflecting the gradient information may be used when the fuel consumption estimation unit 140 estimates fuel consumption when generating statistical data of the second processor 130.
  • the road attribute information reflecting unit 123 is on a map of GPS coordinates representing the driving route of the vehicle in the driving data refined by the preprocessor 110. Selects a matching link for GPS coordinates based on a location of the target, assigns an ID of the selected matching link as a link ID of GPS coordinates, and road attribute information corresponding to the GPS coordinates from map data based on the assigned link ID; Can be obtained and reflected in the driving data.
  • the road property information reflecting unit 123 may first calculate a distance from the previous GPS coordinates for each of the GPS coordinates (S2330), and determine whether the calculated distance meets a preset criterion (S2330). S2331).
  • the criterion is, for example, for determining whether a new search for a matching link is required according to the distance difference between GPS coordinates, and may be set to an arbitrary value by the user.
  • the road attribute information reflecting unit 123 maps the GPS coordinates (hereinafter, referred to as the m GPS coordinates, where m is a natural number) for convenience of explanation if the calculated distance meets a preset criterion.
  • Information corresponding to the data can be converted (S2332). Since a value indicating an arbitrary place in the map data and a format of the m th GPS coordinate value may be different, the road attribute information reflecting unit 123 may use the m th GPS coordinate as information corresponding to the map data (hereinafter, referred to as “m”). , Referred to as a 'spatial index'.
  • the road attribute information reflecting unit 123 may use a spatial index corresponding to the m th GPS coordinates to form a candidate link adjacent to the m th GPS coordinates in the map data (that is, the candidate road on which the m th GPS coordinates may be located). ) Can be selected (S2333).
  • the road attribute information reflecting unit 123 may calculate at least one of a distance weight, a history weight, and a speed weight for the selected candidate links (S2334).
  • the road attribute information reflecting unit 123 may calculate a distance weight for each of the candidate links based on the distance between the m th GPS coordinate and the candidate links.
  • the distance weight may be information corresponding to a distance between the m th GPS coordinate and the candidate links.
  • the distance weight may include a candidate link having the smallest distance from the m-th GPS coordinate among the candidate links, but is not limited thereto.
  • the road attribute information reflecting unit 123 may include a history weight for each of the candidate links based on the number of times the candidate links have been previously assigned as link IDs for GPS coordinates other than the mth GPS coordinates. Can be calculated.
  • the history weight may be information corresponding to the number of times assigned as the link ID for the other GPS coordinates.
  • the history weight may have the smallest value of the candidate link most allocated to the link ID of other GPS coordinates among the candidate links, but is not limited thereto.
  • the road attribute information reflecting unit 123 further adds a speed weight of each of the candidate links through a comparison between the vehicle's running speed at the mth GPS coordinates and a road speed limit at the candidate links. Can be calculated.
  • the speed weight may be information about a speed difference of the vehicle in the speed limit of the candidate links and the m-th GPS coordinate.
  • the speed weight may include, but is not limited to, a candidate link having the smallest difference among the candidate links.
  • the road attribute information reflecting unit 123 may calculate and use various weights in addition to the above-described distance weights, history weights, and speed weights, and select the matching link, which will be described later. It is noted that the unit 123 uses the distance weight, the history weight, and the speed weight as an example.
  • the road attribute information reflecting unit 123 may select a matching link among the candidate links based on the calculated weight value (S2335).
  • the road attribute information reflecting unit 123 may select a matching link matching the mth GPS coordinate among the candidate links based on any one of a distance weight, a history weight, a speed weight, or a combination thereof. Can be selected.
  • the road attribute information reflecting unit 123 may select a candidate link having the smallest distance weight among the candidate links as the matching link.
  • the candidate link closest to the mth GPS coordinate may be selected as the matching link.
  • the road attribute information reflecting unit 123 may select a candidate link having the smallest sum of the distance weight and the history weight among the candidate links as the matching link. Even if one of the candidate links is the link that is not assigned to the other GPS coordinates even if the candidate link is the closest link to the mth GPS coordinates, it may be difficult to consider the position on the actual driving route. In further consideration, the candidate link corresponding to the actual driving route may be selected as the matching link.
  • the road attribute information reflecting unit 123 may select a candidate link having the smallest sum of the distance weight and the speed weight among the candidate links as the matching link.
  • the speed information corresponding to the mth GPS coordinate is 110 [km / h]
  • the speed limit of the K local road is 80 [km / h]
  • the speed limit of the U highway is 110 [km / h].
  • the candidate link may be selected as the matching link.
  • the road attribute information reflecting unit 123 may assign the ID of the selected matching link as the link ID of the m th GPS coordinate (S2336), and from the map data to the m th GPS coordinate based on the assigned link ID.
  • the road property information may be obtained (S2337).
  • the road attribute information reflecting unit 123 may allocate an ID of a link assigned to the previous GPS coordinates as a link ID of the corresponding GPS coordinates (S2338).
  • the road attribute information reflecting unit 123 may determine an ID of a link assigned to the m th GPS coordinate if the distance between the m + 1 th GPS coordinate and the m th GPS coordinate does not meet a preset criterion.
  • the link ID of the m + 1 th GPS coordinate may be allocated. Through this, the road attribute information reflecting unit 123 may improve data matching speed. Subsequently, the road property information reflecting unit 123 may obtain road property information for the m + 1th GPS coordinates from map data based on the assigned link ID (S2337).
  • the road attribute information reflecting unit 123 may reflect the obtained road attribute information in the driving data purified by the preprocessor 110 (S2339).
  • road attribute information may include information on a road type, a road facility type, a road width, a driver, a speed limit, and the like.
  • FIG. 8B is a diagram for explaining a case where road attribute information is reflected in the driving data purified by the preprocessor 110. Therefore, the driving data reflecting the road property information may include information on a vehicle number, date, time, GPS coordinates, link ID, road name, road type, car player, speed limit, and the like.
  • the second processor 130 may generate statistical data by statistically analyzing driving data in which slope information and / or road property information is reflected (S251).
  • the second processor 130 may calculate an average value of records of at least one field of the driving data, calculate a cumulative value of the calculated average value, perform data mining from a short term / long term perspective, or the like. Through the same statistical calculation and analysis, statistical data including various driving pattern information of the driver may be generated.
  • the driving data may include a mileage, a travel time, a data acquisition period, a data acquisition date and time, a speed, an engine revolutions per minute (RPM), a brake signal, a position, an azimuth, an acceleration, a manifold absolute pressure (MAP), and a MAF (Mass).
  • Air flow fuel injection amount, road name, road type, road facility type, number of lanes, road width, road speed limit, toll road status field, road slope may include a record for at least one field.
  • the statistical data includes average speed, average engine revolutions per minute (RPM), average stop time, number of stops, speed standard deviation, RPM standard deviation, speed increase standard deviation, speed reduction standard deviation, speed and RPM correlation coefficient, vehicle speed GPS speed difference, number of speeds, number of dangerous speeds, number of rapid accelerations, number of sudden decelerations, number of sudden starts, number of sudden stops, idlings, speed section ratio, RPM section ratio, fuel consumption, fuel level, It may include a record for at least one of the fuel economy, carbon dioxide generation amount and the driving mode field.
  • RPM revolutions per minute
  • FIG. 10 is a diagram illustrating an example of fields of driving data and statistical data
  • FIG. 11 is a diagram illustrating an example of statistical data.
  • the second processor 130 may generate statistical data illustrated in FIGS. 10 and 11 by using driving data in which road attribute information and / or slope information illustrated in FIG. 10 is reflected.
  • the statistical data generated by the second processing unit 130, the analysis data generated during the statistical analysis, etc. may be stored in the database 160, provided to the user through the user interface unit 150, an external device, It may be sent to an external system.
  • the fuel consumption estimator 140 uses a data analysis technique, for example, supervised, using at least one of a field of driving data and statistical data reflecting slope information and / or road attribute information as a variable. learning may be used to generate a fuel consumption estimation model (S271).
  • a data analysis technique for example, supervised, using at least one of a field of driving data and statistical data reflecting slope information and / or road attribute information as a variable. learning may be used to generate a fuel consumption estimation model (S271).
  • the fuel consumption estimator 140 performs a regression analysis using at least one of the fields of the driving data and the statistical data as independent variables, and a field related to fuel consumption among the fields of the driving data as dependent variables. Through the fuel consumption estimation model can be generated.
  • the present invention is not limited thereto, and the fuel consumption estimator 140 may use various supervised learning analysis techniques such as non-linear regression, support vector machines, and neural networks. A fuel consumption estimation model can be generated.
  • the fuel consumption estimator 140 may use complex driving data and statistical data associated with various drivers and various types of vehicles when generating the fuel consumption estimation model through the supervised learning analysis technique, but are not limited thereto. According to an embodiment, the fuel consumption estimator 140 may classify the driving data and the statistical data into predetermined units such as a specific driver and a specific vehicle, and then generate a fuel consumption estimation model for each predetermined unit.
  • the fuel consumption estimating unit 140 may estimate the fuel consumption of the vehicle on a predetermined time basis by using the fuel consumption estimation model (S273).
  • the fuel consumption estimating unit 140 applies at least one of driving data of a fuel consumption estimation target (hereinafter referred to as a target vehicle) and field records of statistical data to the fuel consumption estimation model, for a predetermined time.
  • the fuel consumption of the target vehicle may be estimated in units, and the estimation result may be output as result data.
  • the record of the field applied to the fuel consumption estimation model is not limited to a record of a field directly or indirectly associated with fuel, such as fuel ejection amount of the driving data, fuel consumption amount of the statistical data, fuel remaining amount, and the like.
  • the fuel consumption estimator 140 may estimate the fuel consumption of the target vehicle only with a record of a field that is not directly or indirectly associated with the fuel.
  • the fuel consumption estimating unit 140 may estimate the fuel consumption in various time units such as seconds and minutes.
  • the result data generated by the fuel consumption estimation for 1 second can be used for the economic driving index for each vehicle / driver, and the result data generated by the fuel consumption estimation for 10 seconds / 1 minute / 5 minutes is transmitted to the link unit network.
  • Matching can be used to guide the route to minimize the fuel consumption, and the result data generated by the fuel consumption estimate per day / month may be used for the purpose of preventing the driver's fuel flow misuse by each transport company.
  • FIG. 13 is a view schematically illustrating an environment in which a fuel consumption estimation system is used, according to an embodiment of the inventive concept. Since the fuel consumption estimating system 1300 illustrated in FIG. 13 is substantially the same as the fuel consumption estimating system 100 illustrated in FIG. 1, a redundant description will be omitted for convenience of description.
  • the fuel consumption estimation result data for the specific driver, vehicle type, and driving route obtained by the fuel consumption estimation system 1300 may include at least one of the eco driving system 1310, the eco routing system 1330, and the fuel cost evaluation system 1350. Can be delivered as one.
  • the eco driving system 1310 may improve the driving habits of the driver based on the result data about the fuel consumption estimated based on the driving data, the statistical data, or the like, or the mining result data for analyzing the driving pattern.
  • the eco driving system 1310 may be implemented in a mobile terminal of a vehicle driver, navigation mounted in a vehicle, a transportation company management system, and the like.
  • the eco routing system 1330 may contribute to the driver's fuel savings by providing the driver with a driving route that minimizes fuel consumption based on the result data regarding the estimated fuel consumption.
  • the eco routing system 1330 may be implemented in a mobile terminal of a vehicle driver, a navigation mounted in a vehicle, or the like, and may be implemented in a system such as a traffic safety management corporation.
  • the oil cost evaluation system 1330 may be implemented in a control system of a national agency, a transportation company, or the like, and may evaluate whether or not fraudulent supply of oil subsidies supported for commercial vehicles is performed using the resultant data on estimated fuel consumption.
  • the fuel consumption estimation result obtained by the fuel consumption estimation system 1300 may be transmitted to, for example, an environmental management system for CO2 emission regulation and management.

Landscapes

  • Traffic Control Systems (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Control Of Vehicle Engines Or Engines For Specific Uses (AREA)
  • Navigation (AREA)

Abstract

L'invention concerne un système pour estimer la consommation de carburant selon un aspect de l'idée technique de la présente invention, le système comprenant : une unité de prétraitement pour affiner des données de conduite obtenues à partir d'un véhicule ; une première unité de traitement pour obtenir des informations d'attributs de route correspondant à un trajet de conduite du véhicule sur la base des données de conduite affinées et de données de carte et refléter les informations d'attributs de route sur les données de conduite affinées ; une seconde unité de traitement pour produire des données statistiques sur la base des données de conduite sur lesquelles les informations d'attributs de route sont réfléchies ; et une unité d'estimation de consommation de carburant pour estimer la consommation de carburant du véhicule sur la base des données de conduite sur lesquelles les informations d'attributs de route sont réfléchies et/ou des données statistiques produites.
PCT/KR2017/003485 2016-03-30 2017-03-30 Système d'estimation de consommation de carburant sur la base d'une grande analyse de données spatiale Ceased WO2017171429A2 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR10-2016-0038779 2016-03-30
KR20160038779 2016-03-30
KR1020170040080A KR101932695B1 (ko) 2016-03-30 2017-03-29 공간 빅 데이터 분석 기반의 연료 소모량 추정 시스템
KR10-2017-0040080 2017-03-29

Publications (2)

Publication Number Publication Date
WO2017171429A2 true WO2017171429A2 (fr) 2017-10-05
WO2017171429A3 WO2017171429A3 (fr) 2018-08-02

Family

ID=59966181

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2017/003485 Ceased WO2017171429A2 (fr) 2016-03-30 2017-03-30 Système d'estimation de consommation de carburant sur la base d'une grande analyse de données spatiale

Country Status (1)

Country Link
WO (1) WO2017171429A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111080202A (zh) * 2019-12-13 2020-04-28 拉货宝网络科技有限责任公司 一种面向油量节省的作业车辆的效能管理方法及系统
CN112857495A (zh) * 2021-01-22 2021-05-28 陕西天行健车联网信息技术有限公司 一种基于车联网大数据的车辆油耗统计方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4442290B2 (ja) * 2004-04-02 2010-03-31 株式会社デンソー 車載ナビゲーション装置
JP4955043B2 (ja) * 2009-10-06 2012-06-20 本田技研工業株式会社 燃費情報管理サーバ、燃費情報管理システムおよび燃費情報管理方法
KR101231515B1 (ko) * 2010-06-30 2013-02-07 기아자동차주식회사 주행경로의 연료량 계산 시스템 및 그 방법
KR101386986B1 (ko) * 2011-12-27 2014-04-18 주식회사 디지털존 경제운전 정보를 제공하는 방법 및 장치
KR101601031B1 (ko) * 2014-05-02 2016-03-08 국민대학교 산학협력단 운행기록 빅데이터 처리 및 분석 방법

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111080202A (zh) * 2019-12-13 2020-04-28 拉货宝网络科技有限责任公司 一种面向油量节省的作业车辆的效能管理方法及系统
CN112857495A (zh) * 2021-01-22 2021-05-28 陕西天行健车联网信息技术有限公司 一种基于车联网大数据的车辆油耗统计方法

Also Published As

Publication number Publication date
WO2017171429A3 (fr) 2018-08-02

Similar Documents

Publication Publication Date Title
KR101932695B1 (ko) 공간 빅 데이터 분석 기반의 연료 소모량 추정 시스템
JP7295036B2 (ja) トリップの種類を識別するためのテレマティクスデータの使用
EP3756056A1 (fr) Véhicule utilisant des informations spatiales acquises à l'aide d'un capteur, dispositif de détection utilisant des informations spatiales acquises à l'aide d'un capteur, et serveur
WO2023128406A1 (fr) Serveur de gestion prenant en charge une surveillance de sécurité et un domaine opérationnel de conception (odd) de véhicule autonome connecté
WO2022114453A1 (fr) Procédé d'entrainement de réseau neuronal artificiel pour prédire si un véhicule est en panne, procédé pour déterminer si un véhicule est en panne, et système informatique le mettant en œuvre
WO2020141694A1 (fr) Véhicule utilisant des informations spatiales acquises à l'aide d'un capteur, dispositif de détection utilisant des informations spatiales acquises à l'aide d'un capteur, et serveur
WO2021060778A1 (fr) Véhicule et procédé de génération de carte correspondant à un espace en trois dimensions
CN109637170A (zh) 驾驶辅助装置、信息处理装置、驾驶辅助系统和驾驶辅助方法
WO2022050487A1 (fr) Procédé et dispositif de support de conduite
WO2022250471A1 (fr) Procédé et appareil pour déterminer un réseau de ligne médiane de voie de circulation
WO2017171429A2 (fr) Système d'estimation de consommation de carburant sur la base d'une grande analyse de données spatiale
WO2023027361A1 (fr) Procédé et appareil de détermination d'un trajet de déplacement d'un véhicule en tenant compte du flux de déplacement de passagers
CN109383512A (zh) 用于运行自动化移动系统的方法和设备
WO2013115525A1 (fr) Dispositif destiné à obtenir des informations en temps réel par utilisation d'une analyse factorielle en fonction de conditions de route et de circulation et procédé pour cela
WO2021045445A1 (fr) Dispositif de traitement d'examen de permis de conduire d'un conducteur
WO2019124668A1 (fr) Système d'intelligence artificielle pour fournir des informations de danger de surface de route et procédé associé
WO2022255677A1 (fr) Procédé de détermination d'emplacement d'objet fixe à l'aide d'informations multi-observation
WO2020241971A1 (fr) Dispositif de gestion d'accident de la circulation et procédé de gestion d'accident de la circulation
WO2021256636A1 (fr) Procédé de gestion de trafic sur la base d'un réseau de chaîne de blocs, et dispositif et système pour mettre en œuvre ce procédé
WO2022270751A1 (fr) Procédé et dispositif de détection de surface de chaussée à l'aide d'un capteur lidar
WO2021045246A1 (fr) Appareil et procédé de fourniture d'une fonction étendue à un véhicule
WO2022197042A1 (fr) Reconnaissance d'entrée dans un carrefour illégale et dispositif de stockage d'images
WO2022255678A1 (fr) Procédé d'estimation d'informations d'agencement de feux de circulation faisant appel à de multiples informations d'observation
WO2015170794A1 (fr) Dispositif et procédé de détection de véhicules environnants
CN115667848A (zh) 用于将车辆的gnss位置进行地图匹配的系统和方法

Legal Events

Date Code Title Description
NENP Non-entry into the national phase in:

Ref country code: DE

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17775853

Country of ref document: EP

Kind code of ref document: A2

122 Ep: pct application non-entry in european phase

Ref document number: 17775853

Country of ref document: EP

Kind code of ref document: A2