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WO2019028819A1 - Procédé de positionnement d'un véhicule et dispositifs associés - Google Patents

Procédé de positionnement d'un véhicule et dispositifs associés Download PDF

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Publication number
WO2019028819A1
WO2019028819A1 PCT/CN2017/097041 CN2017097041W WO2019028819A1 WO 2019028819 A1 WO2019028819 A1 WO 2019028819A1 CN 2017097041 W CN2017097041 W CN 2017097041W WO 2019028819 A1 WO2019028819 A1 WO 2019028819A1
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WO
WIPO (PCT)
Prior art keywords
traffic data
target vehicle
location
moment
target
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/CN2017/097041
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English (en)
Chinese (zh)
Inventor
阳光
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.)
Shenzhen A&e Smart Institute Co Ltd
Original Assignee
Shenzhen A&e Smart Institute Co Ltd
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
Application filed by Shenzhen A&e Smart Institute Co Ltd filed Critical Shenzhen A&e Smart Institute Co Ltd
Priority to CN201780092649.2A priority Critical patent/CN110832277B/zh
Priority to PCT/CN2017/097041 priority patent/WO2019028819A1/fr
Publication of WO2019028819A1 publication Critical patent/WO2019028819A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention relates to the field of communications, and in particular, to a method for locating a vehicle and related devices.
  • the vehicle system needs to obtain traffic data from the cloud database to help the vehicle system provide accurate maps, geographic information, and clear travel routes.
  • the vehicle system needs to accurately locate the target vehicle when acquiring traffic data from the cloud database.
  • the vehicle system often cannot effectively obtain the current precise position in the road scene.
  • the in-vehicle system transmits information to 3 to 4 satellites, and calculates the current position by receiving the arrival time difference of the information of the reply of 3 to 4 satellites;
  • Positioning technology based on image recognition The vehicle system obtains the relative positional deviation between the current position and the landmark building by identifying the landmark building, the current azimuth angle and the like. Then get the location of the landmark through the cloud database and calculate the current location.
  • GPS-based positioning technology is susceptible to environmental influences. For example, in some complicated road sections or severely disturbed environments, the in-vehicle system cannot connect to the network, nor can it receive the information transmitted by the satellite, and thus cannot calculate the current time difference by the information. position. Based on the image recognition-based positioning technology, when there is no landmark building around the location, there is no reference information, and accurate positioning information cannot be obtained at this time.
  • Embodiments of the present invention provide a positioning method of a vehicle and related equipment for improving the influence of the environment and the inaccurate positioning when the landmark building is lacking.
  • a first aspect of the embodiments of the present invention provides a method for locating a vehicle, including:
  • the positioning position of the second moment is transmitted to the target vehicle.
  • a second aspect of the embodiments of the present invention provides a method for locating a vehicle, including:
  • first traffic data being traffic data collected by the target vehicle at the first moment
  • the cloud server determines, according to the positioning position of the first moment, the traveling direction of the target vehicle, and the speed information, that the target vehicle is a positioning position at a second time, the second time being later than the first time;
  • a third aspect of the embodiments of the present invention provides a method for locating a vehicle, including:
  • Transmitting traffic data of the target road segment to the target vehicle such that the target vehicle compares traffic data collected at the target road segment with traffic data of a target road segment sent by the cloud server to determine the The location of the target vehicle in the target road segment.
  • a fourth aspect of the embodiments of the present invention provides a method for locating a vehicle, including:
  • first traffic data being traffic data collected by the target vehicle at a first moment
  • the cloud server determines, according to the first traffic data, a positioning location of the target vehicle at a first moment, according to the location location of the target vehicle at the first moment
  • the traffic data of the target road segment is sent to the target vehicle, and the target road segment includes at least a road segment whose distance from the positioning position of the target vehicle at the first time is within a preset range;
  • a fifth aspect of the embodiments of the present invention provides a server, including:
  • a central processing unit a storage medium, and an input and output interface
  • the program code stored on the storage medium the central processor is configured to invoke the program code to perform the following steps:
  • the positioning position of the second moment is transmitted to the target vehicle.
  • a sixth aspect of the embodiments of the present invention provides a server, including:
  • a central processing unit a storage medium, and an input and output interface
  • the program code stored on the storage medium the central processor is configured to invoke the program code to perform the following steps:
  • Transmitting traffic data of the target road segment to the target vehicle such that the target vehicle compares traffic data collected at the target road segment with traffic data of a target road segment sent by the cloud server to determine the The location of the target vehicle in the target road segment.
  • a seventh aspect of the embodiments of the present invention provides an in-vehicle client, including:
  • a central processing unit a storage medium, and an input and output interface
  • the program code stored on the storage medium the central processor is configured to invoke the program code to perform the following steps:
  • first traffic data being traffic data collected by the target vehicle at the first moment
  • the cloud server determines, according to the positioning position of the first moment, the traveling direction of the target vehicle, and the speed information, that the target vehicle is a positioning position at a second time, the second time being later than the first time;
  • An eighth aspect of the embodiments of the present invention provides an in-vehicle client, including:
  • a central processing unit a storage medium, and an input and output interface
  • the program code stored on the storage medium the central processor is configured to invoke the program code to perform the following steps:
  • first traffic data being traffic data collected by the target vehicle at a first moment
  • the cloud server determines, according to the first traffic data, a positioning location of the target vehicle at a first moment, according to the location location of the target vehicle at the first moment
  • the traffic data of the target road segment is sent to the target vehicle, and the target road segment includes at least a road segment whose distance from the positioning position of the target vehicle at the first time is within a preset range;
  • the server determines, according to the first traffic data acquired from the target vehicle, the positioning position of the target vehicle at the first moment, according to the first The positioning position at a moment and the traveling direction and speed information of the target vehicle determine the positioning position of the target vehicle at the second time, and transmit the positioning position of the second time to the target vehicle. Since the positioning position determined by the matching is determined according to the traffic data collected by the target vehicle in the embodiment of the present invention, the accurate position is determined by directly using the GPS positioning technology or the relative position deviation of the landmark building to determine the precise position. The problem of inaccurate positioning due to the influence of the environment and the lack of landmark buildings.
  • FIG. 1 is a schematic diagram of an embodiment of a method for positioning a vehicle according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of another embodiment of a method for positioning a vehicle according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of another embodiment of a method for positioning a vehicle according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of another embodiment of a method for positioning a vehicle according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of another embodiment of a method for positioning a vehicle according to an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of another embodiment of a method for positioning a vehicle according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of hardware of an in-vehicle client according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of hardware of a server according to an embodiment of the present invention.
  • an embodiment of a method for locating a vehicle in an embodiment of the present invention includes:
  • the cloud server may receive the first traffic data sent by the target vehicle, where the first traffic data is traffic data of the location of the target vehicle collected at the first time, and the first traffic data is the vehicle customer.
  • the traffic data collected at the first moment, the traffic data includes visual data and other data, such as weather data and altitude data of the current road segment.
  • the traffic data may also include location information, such as GPS location information.
  • the cloud server may match the traffic data in the cloud database according to the first traffic data to determine a positioning position of the target vehicle at the first moment.
  • the cloud server includes a cloud database, and the cloud server is connected to the target vehicle through a wireless manner.
  • the cloud database stores traffic data of each location within a certain range, and at least includes traffic data of a location where the target vehicle collects the first traffic data.
  • the cloud server may also extract feature data in the first traffic data, match the feature data in the first traffic data with the feature data in the cloud database, and obtain feature data in the cloud database with the highest matching degree.
  • the corresponding position is used as the positioning position of the target vehicle at the first moment, and the position corresponding to the feature data in the cloud database whose matching degree reaches the preset value may also be used as the position corresponding to the feature data in the cloud database with the matching degree reaching the preset value.
  • the positioning position of the target vehicle at the first moment is not specifically limited.
  • the first traffic data does not include location data
  • the cloud server matches the received first traffic data with traffic data of all locations stored therein to determine that the target vehicle is in the first The location of the moment.
  • the cloud server first extracts visual data and location data in the first traffic data, and then according to the location.
  • the data is filtered from the cloud database for visual data of each location within a preset range, and the visual data in the first traffic data is matched with the visual data of all the selected locations to obtain the The location of the target vehicle at the first moment.
  • the preset range may be a range in which the distance from the position data in the first traffic data is a preset distance (for example, 1000 meters).
  • the cloud server may acquire the traveling direction and speed information sent by the target vehicle, and the traveling direction and speed information of the target vehicle are acquired when the target vehicle is at the first time, and the driving direction and the speed information are sent.
  • step 103 can be performed simultaneously with step 101.
  • the cloud server may calculate the distance traveled between the first time and the second time by the target vehicle by using the first time and the speed information, and pass the first time position and the target vehicle at the first time and the second time. The distance traveled between the times determines the position of the target vehicle at the second moment.
  • the cloud server can extend the time interval between the second time and the first time according to the weather conditions in the traffic data, such as foggy weather, heavy rain or heavy snow weather, and the cloud data needs to be matched by the cloud database. Extending the time interval between the second time and the first time may help the cloud server match the traffic data sent by the target vehicle to a more precise positioning position.
  • the cloud server may send the location location of the second moment to the target vehicle.
  • the server when it is necessary to determine the precise location of the target vehicle, the server is obtained from the target vehicle. Determining the first traffic data to determine a positioning position of the target vehicle at the first moment, determining a positioning position of the target vehicle at the second moment according to the positioning position at the first moment and the traveling direction and speed information of the target vehicle, and the second position The position of the moment is sent to the target vehicle. Since the positioning position of the target vehicle determined according to the traffic data, the traveling direction and the speed information collected by the target vehicle in the embodiment of the present invention, the GPS positioning technology is directly used with respect to the prior art or the relative position deviation of the landmark building is used to determine the accuracy. In terms of location, it effectively improves the environmental impact and the inaccuracy of positioning when there is no landmark building.
  • FIG. 2 another embodiment of a method for locating a vehicle according to an embodiment of the present invention includes:
  • first traffic data is traffic data collected by the target vehicle at the first moment.
  • the in-vehicle client of the target vehicle may collect the first traffic data through the visual system, where the first traffic data is the traffic data collected by the in-vehicle client at the first moment, Traffic data includes visual data as well as other data such as weather data and altitude data for the current road segment.
  • the in-vehicle client acquires the current traveling direction and speed information of the target vehicle through the vision system.
  • the second moment is later than the first moment.
  • the second time is a time when the predicted target vehicle receives the positioning position information.
  • the in-vehicle client can receive the location location of the second moment sent by the cloud server.
  • the positioning position of the target vehicle is the number of traffic that the vehicle-mounted client will collect at the first moment.
  • the driving direction and the speed information are sent to the cloud server, so that the cloud server determines the relative position deviation of the landmark building directly according to the prior art by using the GPS positioning technology according to the collected traffic data, the driving direction and the speed information. Determining the precise location effectively improves the environmental impact and the inaccurate positioning of the landmark building.
  • another embodiment of a method for locating a vehicle in an embodiment of the present invention includes:
  • the cloud server receives first traffic data sent by the target vehicle, where the first traffic data is traffic data collected by the target vehicle at a first moment.
  • the cloud server may receive first traffic data sent by the target vehicle, where the first traffic data is traffic data collected by the target vehicle at the first moment, and the traffic data may include visual data and some other data, such as weather. Data and altitude data of the current road segment, etc.
  • the traffic data may also include location information, such as GPS location information.
  • the cloud server matches the first traffic data with the traffic data in the cloud database to obtain a location location of the target vehicle at the first moment.
  • the cloud server may match the traffic data in the cloud database according to the first traffic data to determine a positioning position of the target vehicle at the first moment.
  • the cloud server includes a cloud database, and the cloud server is wirelessly connected to the target vehicle.
  • the cloud database stores traffic data of various locations within a certain range, and at least includes traffic data of a location where the target vehicle collects the first traffic data.
  • the cloud server may also extract feature data in the first traffic data, match the feature data in the first traffic data with the feature data in the cloud database, and obtain feature data in the cloud database with the highest matching degree.
  • the corresponding position is used as the positioning position of the target vehicle at the first time, and the position corresponding to the feature data in the cloud database with the matching degree reaching the preset value may be used as the positioning position of the target vehicle at the first time, which is not limited.
  • the first traffic data does not include location data
  • the cloud server matches the received first traffic data with traffic data of all locations stored therein to determine that the target vehicle is in the first The location of the moment.
  • the cloud server first extracts visual data and location data in the first traffic data, and then according to the location. Data from the cloud database Filtering visual data of each location within the preset range, and matching the visual data in the first traffic data with the visual data of all the selected locations to obtain the target vehicle at the first moment Position the location.
  • the preset range may be a range in which the distance from the position data in the first traffic data is a preset distance (for example, 1000 meters).
  • the cloud server extracts traffic data of the target road segment from the cloud database according to the location position of the target vehicle at the first time, and the target road segment includes at least a road segment whose distance from the positioning position of the target vehicle at the first time is within a preset range.
  • the road segment within the preset range from the positioning position at the first moment may be defined as the target road segment, and the cloud server extracts from the cloud database. Traffic data corresponding to the target road segment.
  • the cloud server sends the traffic data of the target road segment to the vehicle-mounted client of the target vehicle, so that the target vehicle compares the traffic data collected on the target road segment with the traffic data of the target road segment sent by the cloud server. Determining a location of the target vehicle in the target road segment.
  • the cloud server receives the first traffic data sent by the in-vehicle client of the target vehicle, and the cloud server determines the location location of the target vehicle at the first moment according to the traffic data at the first moment, and according to the target vehicle at the first moment.
  • the positioning position extracts traffic data of the target road segment from the cloud database, and the target road segment includes at least a road segment within a preset range from the positioning position of the target vehicle at the first moment, and transmits the traffic data of the target road segment to the target vehicle,
  • the target vehicle is compared with the traffic data collected on the target road segment and the traffic data of the target road segment sent by the cloud server to determine a positioning position of the target vehicle in the target road segment.
  • the GPS positioning technology is directly used or the relative position deviation of the landmark building is used to determine the precise position, which effectively improves the environmental impact and the inaccurate positioning when the landmark building is lacking.
  • FIG. 4 is another embodiment of a method for locating a vehicle according to an embodiment of the present invention, including:
  • the vehicle client collects first traffic data, where the first traffic data is traffic data collected by the target vehicle at the first moment.
  • the in-vehicle client of the target vehicle may collect the first traffic data, where the first traffic data is traffic data collected by the in-vehicle client of the target vehicle at the first moment, and the traffic data may be Includes visual data and other data such as weather data and altitude data for the current road segment.
  • the in-vehicle client sends the first traffic data to the cloud server, so that the cloud server determines, according to the first traffic data, a positioning location of the target vehicle at a first moment, according to the target vehicle at the first moment.
  • the positioning position transmits traffic data of the target road segment to the target vehicle, the target road segment including at least a road segment within a preset range from a positioning position of the target vehicle at the first time.
  • the vehicle client receives traffic data of the target road segment.
  • the in-vehicle client can receive traffic data of the target road segment.
  • the in-vehicle client determines, according to the traffic data collected by the target vehicle on the target road segment and the traffic data of the target road segment sent by the cloud server, the location position of the target vehicle in the target road segment.
  • the vehicle client After receiving the traffic data of the target road segment sent by the cloud server, the vehicle client can collect the current traffic data of the target vehicle in real time and match the traffic data of the target road segment sent by the cloud database, and determine the current positioning position of the target vehicle.
  • the vehicle-mounted client of the target vehicle collects the traffic data of the first moment, and sends the destination information of the target vehicle to the cloud server, and the cloud server determines the location of the target vehicle at the first moment according to the traffic data at the first moment.
  • the location and the traffic data of the target road segment are sent to the target vehicle.
  • the current traffic data can be collected in real time to match the traffic data of the target road segment sent by the cloud database to determine the positioning position of the target vehicle.
  • the GPS positioning technology is directly used or the relative position deviation of the landmark building is used to determine the precise position, which effectively improves the environmental impact and the inaccurate positioning when the landmark building is lacking.
  • the cloud server determines the positioning position of the target vehicle at the first moment according to the traffic data of the first moment, and the positioning position of the vehicle may be The cloud server determines the positioning position of the vehicle at the second moment according to the positioning position at the first moment, the traveling direction of the vehicle, and the speed information, and may also determine the vehicle by the vehicle-mounted client of the vehicle through the collected traffic data and the acquired traffic data.
  • the location of the target road segment is explained below:
  • the cloud server determines the positioning position of the vehicle at the second moment according to the positioning position at the first moment, the traveling direction of the vehicle, and the speed information.
  • another embodiment of a method for locating a vehicle in an embodiment of the present invention includes:
  • the vehicle client collects traffic data of the target vehicle at the first moment.
  • the vehicle-mounted client may collect the first traffic data of the target vehicle through the vision system, where the first traffic data is the traffic data collected by the vehicle-mounted client at the first moment.
  • the first traffic data is traffic data collected by the in-vehicle client at a first moment, and the traffic data includes visual data and other data, such as weather data and altitude data of the current road segment.
  • the traffic data may also include location information, such as GPS location information.
  • the in-vehicle client sends the first traffic data to the cloud server.
  • the in-vehicle client may send the first traffic data to the cloud server.
  • the cloud server matches the first traffic data with the traffic data in the cloud database to determine a location location of the target vehicle at the first moment.
  • the cloud server may match the first traffic data with the traffic data in the cloud database to determine the location of the target vehicle at the first moment, that is, The first traffic data is matched with the traffic data of each location stored in the cloud database, and the location location corresponding to the traffic data with the highest degree of matching of the first traffic data in the cloud database is determined as the location location at the first moment, in the cloud server
  • the cloud database is included, and the cloud server is connected to the target vehicle through a wireless manner.
  • the cloud database stores traffic data of various locations within a certain range, and at least includes traffic data of a location where the target vehicle collects the first traffic data.
  • the cloud server may also extract feature data in the first traffic data, match the feature data in the first traffic data with the feature data in the cloud database, and obtain feature data in the cloud database with the highest matching degree.
  • the corresponding position is used as the positioning position of the target vehicle at the first time, and the position corresponding to the feature data in the cloud database with the matching degree reaching the preset value may be used as the positioning position of the target vehicle at the first time, which is not limited.
  • the first traffic data may be matched with the traffic data of each location stored in the cloud database, and the matching degree of the cloud database with the first traffic data may be matched.
  • the positioning position corresponding to the traffic data reaching the preset value is determined as the positioning position at the first moment.
  • the first traffic data does not include location data
  • the cloud server matches the received first traffic data with traffic data of all locations stored therein to determine that the target vehicle is in the first The location of the moment.
  • the cloud server first extracts visual data and location data in the first traffic data, and then according to the location.
  • the data is filtered from the cloud database for visual data of each location within a preset range, and the visual data in the first traffic data is matched with the visual data of all the selected locations to obtain the The location of the target vehicle at the first moment.
  • the preset range may be a range in which the distance from the position data in the first traffic data is a preset distance (for example, 1000 meters).
  • the cloud server acquires a traveling direction and speed information of the target vehicle.
  • the target vehicle acquires its own driving direction and speed information at the first moment, and transmits the driving direction and speed information to the cloud server, and the cloud server can receive the traveling direction and speed information sent by the target vehicle.
  • step 503 can be performed simultaneously with step 504.
  • the cloud server determines, according to the positioning position of the first moment, the traveling direction of the target vehicle, and the speed information, the positioning position of the target vehicle at the second moment, where the second moment is a time when the predicted target vehicle receives the positioning location information, and the second moment The moment is later than the first moment.
  • the cloud server calculates the distance traveled between the first time and the second time by the target vehicle by using the first time and the speed information, and the second time is later than the first time, and the target vehicle can be determined at this time.
  • the position at the second moment is at the center of the position at the first moment, and the distance traveled by the target vehicle between the first moment and the second moment is on the outer circumference of a circle having a radius, at which time A positioning position of the target vehicle at the second moment is determined.
  • the cloud server can extend the time interval between the second moment and the first moment according to the weather conditions in the traffic data, such as foggy weather, heavy rain or heavy snow weather, and the cloud database.
  • the traffic data that needs to be matched will be large, and extending the time interval between the second moment and the first moment can help the cloud server match the traffic data sent by the target vehicle to a more precise positioning location.
  • the cloud server sends the location location of the second moment to the target vehicle.
  • the vehicle-mounted client of the target vehicle collects the traffic data at the first moment, and sends the traveling direction and speed data of the target vehicle to the cloud server, and the cloud server determines the target vehicle at the first moment according to the traffic data at the first moment. Positioning position, and determining the positioning position of the target vehicle at the second moment according to the positioning position, the traveling direction and the speed information at the first moment. Since the positioning position of the target vehicle is determined according to the traffic data, the traveling direction and the speed information collected by the vehicle-mounted client, the precise positioning is determined by directly using the GPS positioning technology or the relative positional deviation of the landmark building according to the prior art. Effectively improved the impact of the environment and the lack of accurate positioning in the absence of landmark buildings.
  • the vehicle-mounted client of the vehicle determines the positioning position in the target road segment of the vehicle through the traffic data collected by itself and the acquired traffic data.
  • FIG. 6 is another embodiment of a method for locating a vehicle according to an embodiment of the present invention, including:
  • the vehicle client collects traffic data of the target vehicle at the first moment.
  • the vehicle-mounted client may collect the first traffic data of the target vehicle through the vision system, where the first traffic data is the traffic data collected by the vehicle-mounted client at the first moment.
  • the traffic data includes visual data as well as other data such as weather data and altitude data of the current road segment.
  • the traffic data may also include location information, such as GPS location information.
  • the in-vehicle client sends the first traffic data to the cloud server.
  • the in-vehicle client may send the first traffic data to the cloud server.
  • the cloud server matches the first traffic data with the traffic data in the cloud database to determine a location location of the target vehicle at the first moment.
  • the cloud server may match the first traffic data with the traffic data in the cloud database to determine the location of the target vehicle at the first moment, that is, The intersection of the first traffic data with each location stored in the cloud database The matching data is matched, and the positioning position corresponding to the traffic data with the highest matching degree of the first traffic data in the cloud database is determined as the positioning position at the first time.
  • the cloud server may extract the feature data in the first traffic data. And using the feature data in the first traffic data to compare with the feature data of each location stored in the cloud database of the cloud server to determine the location of the target vehicle at the first moment, and the cloud server includes the cloud database, the cloud server and the target vehicle.
  • the cloud database stores traffic data of various locations within a certain range, and at least includes traffic data of a location where the target vehicle collects the first traffic data.
  • the first traffic data does not include location data
  • the cloud server matches the received first traffic data with traffic data of all locations stored therein to determine that the target vehicle is in the first The location of the moment.
  • the cloud server first extracts visual data and location data in the first traffic data, and then according to the location.
  • the data is filtered from the cloud database for visual data of each location within a preset range, and the visual data in the first traffic data is matched with the visual data of all the selected locations to obtain the The location of the target vehicle at the first moment.
  • the preset range may be a range in which the distance from the position data in the first traffic data is a preset distance (for example, 1000 meters).
  • the first traffic data may be matched with the traffic data of each location stored in the cloud database, and the matching degree of the cloud database with the first traffic data may be matched.
  • the positioning position corresponding to the traffic data reaching the preset value is determined as the positioning position at the first time.
  • the cloud server extracts traffic data of the target road segment from the cloud database according to the location position of the target vehicle at the first time, and the target road segment includes at least a road segment whose distance from the positioning position of the target vehicle at the first time is within a preset range.
  • the road segment within the preset range from the positioning position at the first moment may be defined as the target road segment, and the cloud server extracts the target from the cloud database. Traffic data corresponding to the road segment.
  • the cloud server sends the traffic data of the target road segment to the vehicle client of the target vehicle.
  • the cloud server may transmit traffic data of the target road segment to the target vehicle.
  • the in-vehicle client determines, according to the traffic data collected by the target vehicle on the target road segment and the traffic data of the target road segment sent by the cloud server, the location position of the target vehicle in the target road segment.
  • the vehicle-mounted client may collect the current traffic data of the target vehicle in real time during the driving process and match the traffic data of the target road segment sent by the cloud database. And determine the current positioning position of the target vehicle.
  • the cloud server receives the first traffic data sent by the in-vehicle client of the target vehicle, and the cloud server determines the location location of the target vehicle at the first moment according to the traffic data at the first moment, and according to the target vehicle at the first moment.
  • the positioning position extracts traffic data of the target road segment from the cloud database, and the target road segment includes at least a road segment within a preset range from the positioning position of the target vehicle at the first moment, and transmits the traffic data of the target road segment to the target vehicle, the target The vehicle compares the traffic data collected on the target road segment with the traffic data of the target road segment sent by the cloud server to determine the location position of the target vehicle in the target road segment.
  • the GPS positioning technology is directly used or the relative position deviation of the landmark building is used to determine the precise position, which effectively improves the environmental impact and the inaccurate positioning when the landmark building is lacking.
  • FIG. 7 is a schematic structural diagram of an in-vehicle client according to an embodiment of the present invention.
  • the car may have a large difference in configuration or performance on the client 700, and may include one or more central processors. (central processing units, CPU) 722 (eg, one or more processors), one or more storage media 730 storing application 742 or data 744 (the storage medium may be one or one storage device in Shanghai, or may be A temporary storage device such as one or more memories may also be used for one or one hard disk, or one or more memories and a hard disk, which are not limited herein. Wherein, the storage medium 730 can be short-term storage or persistent storage.
  • the program stored on the storage medium 730 can include a series of instruction operations on the server. Still further, central processor 722 can be configured to communicate with storage medium 730 to perform a series of instruction operations in storage medium 730 on vehicle-mounted client 700.
  • the in-vehicle client 700 may further include one or more input and output interfaces 758 (the input and output interfaces may be one or more wired or wireless network interfaces, or other input and output interfaces, which are not limited herein), and/or One or more operating systems 741, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
  • operating systems 741 such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
  • the steps performed by the in-vehicle client in the above embodiments may be based on the in-vehicle client structure shown in FIG.
  • FIG. 8 is a schematic structural diagram of a server according to an embodiment of the present invention.
  • the server 800 may have a large difference due to different configurations or performances, and may include one or more central processing units (central processing units). , CPU) 822 (eg, one or more processors), one or more storage media 830 storing application 842 or data 844 (the storage medium may be one or one storage device in Shanghai, or one or more A temporary storage device such as a memory may also be used for one or one hard disk, or one or more memories and a hard disk, which are not limited herein. Wherein, the storage medium 830 can be short-term storage or persistent storage.
  • the program stored on the storage medium 830 can include a series of instruction operations on the server. Still further, central processor 822 can be configured to communicate with storage medium 830, executing a series of instruction operations in storage medium 830 on server 800.
  • the server 800 may further include one or more input and output interfaces 858 (the input and output interfaces may be one or more wired or wireless network interfaces, or other input and output interfaces, which are not limited herein), and/or one or More than one operating system 841, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
  • operating system 841 such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
  • the steps performed by the server in the above embodiment may be based on the server structure shown in FIG.
  • the disclosed system, apparatus, and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separate.
  • the components displayed for the unit may or may not be physical units, ie may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un procédé de positionnement de véhicule et des dispositifs associés, permettant de résoudre le problème associé à un positionnement imprécis dû à une influence environnementale et à une absence de bâtiments de repère. Le procédé comprend: la réception de premières données de trafic envoyées par un véhicule cible, les premières données de trafic étant des données de trafic collectées par le véhicule cible à un premier moment (101); la mise en correspondance des premières données de trafic avec des données de trafic se trouvant dans une base de données en nuage pour obtenir la position de l'emplacement du véhicule cible au premier moment (102); l'acquisition d'une direction de conduite et d'informations de vitesse du véhicule cible (S103); la détermination de la position de l'emplacement du véhicule cible à un second moment en fonction de la position de l'emplacement au premier moment, de la direction de conduite et des informations de vitesse, le second moment étant ultérieur au premier moment (104); et l'envoi de la position de l'emplacement au second moment au véhicule cible (105).
PCT/CN2017/097041 2017-08-11 2017-08-11 Procédé de positionnement d'un véhicule et dispositifs associés Ceased WO2019028819A1 (fr)

Priority Applications (2)

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CN201780092649.2A CN110832277B (zh) 2017-08-11 2017-08-11 一种车辆的定位方法及相关设备
PCT/CN2017/097041 WO2019028819A1 (fr) 2017-08-11 2017-08-11 Procédé de positionnement d'un véhicule et dispositifs associés

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CN113870489B (zh) * 2021-09-10 2023-02-07 摩拜(北京)信息技术有限公司 车辆定位方法、装置及车辆

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