Patent Application
20240159563
This application relates to the field of intelligent driving and the field of maps, and in particular, to a map data processing method and apparatus.
A map can assist a vehicle in performing autonomous driving. The map includes static information and dynamic information. The static information describes a map element whose status does not change with time, for example, road network structure data (such as a lane or a road), and road traffic facility data (such as a traffic sign or a roadside facility). The dynamic information describes events that change with time, for example, a traffic accident event, a traffic congestion event, and a road icing event, and the events are associated. For example, the traffic accident event aggravates a congestion degree of the traffic congestion event. For another example, congestion of a road A may cause congestion of a road B connected to the road A.
Future intelligent driving and intelligent transportation pose higher requirements on richness of map information. However, richness of existing map content cannot fully meet requirements of future use.
This application discloses a map data processing method and apparatus, to quickly index an event associated with a changed event in a map, implement linkage update of the event, and improve map update efficiency and reliability of driving decision-making.
According to a first aspect, this application provides a map generation method. The method includes: generating first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and storing the first information as data of the map.
In the foregoing method, map information including an association relationship between events in the map is generated, and is used as prior information. The map information can meet more abundant use requirements. For example, a change of another event caused by a change of an event may be predicted based on an association relationship between events in the map, and a prediction result may be further used as a corroboration reference of the change of the another event. This helps quickly and accurately complete update of a large quantity of events in the map, and improves map update efficiency and reliability of driving decision-making.
Optionally, after storing the first information, the method further includes: sending the first information to a map using apparatus. It should be noted that the first information may be sent through a hardware interface in a hardware device or a data interface in a software module. The map using apparatus includes but is not limited to a cloud device, a roadside device, a terminal device, or a component, a chip, a software module, a hardware module, or an interface in these devices.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
In the foregoing implementation, the first event and the second event are causally related. If the first event is a cause event of the second event, it indicates that the second event is affected by the first event. If the first event is a result event of the second event, that is, the second event is a cause event of the first event, it indicates that the first event is affected by the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
The association factor may be represented by a floating point number of (0, 1]. A larger value of the association factor indicates a stronger degree of correlation between the first event and the second event. The association factor is a comprehensive index, and may represent the degree of correlation between the first event and the second event overall (including a dimension such as time, space, or attribute).
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
The time correlation factor may be represented by a floating point number of (0, 1]. A larger value of the time correlation factor indicates a stronger degree of time correlation between the first event and the second event.
The degree of time correlation between the first event and the second event may be represented as overlapping duration between a time range of the first event and a time range of the second event, or may be represented as an intersection over union or a time interval between the two time ranges. Therefore, optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
In the foregoing implementation, the overlapping duration, the first intersection over union, and the time interval may all be used to measure the degree of time correlation between the first event and the second event. For example, the time correlation factor may be obtained by performing weighted summation on the overlapping duration, the first intersection over union, and the time interval. When other parameters remain unchanged, larger overlapping duration indicates a larger time correlation factor; when other parameters remain unchanged, a larger first intersection over union indicates a larger time correlation factor; and when other parameters remain unchanged, a smaller time interval indicates a larger time correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the time correlation factor, refer to a physical meaning of the parameter.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
The spatial correlation factor may be represented by a floating point number of (0, 1]. A larger value of the spatial correlation factor indicates a stronger degree of spatial correlation between the first event and the second event.
The degree of spatial correlation between the first event and the second event may be represented as an overlapping area, an interval distance, or an intersection over union between location data covered by the first event and location data covered by the second event, or may be represented as a degree of traffic reachability between the two pieces of location data. Therefore, optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
In the foregoing implementation, the overlapping area, the second intersection over union, the interval distance, and the degree of traffic reachability may all be used to measure the degree of spatial correlation between the first event and the second event. For example, the spatial correlation factor may be obtained by performing weighted summation on the overlapping area, the second intersection over union, the interval distance, and the degree of traffic reachability. When other parameters remain unchanged, a larger overlapping area indicates a larger spatial correlation factor; when other parameters remain unchanged, a larger second intersection over union indicates a larger spatial correlation factor; when other parameters remain unchanged, a smaller interval distance indicates a larger spatial correlation factor; and when other parameters remain unchanged, a larger degree of traffic reachability indicates a larger spatial correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the spatial correlation factor, refer to a physical meaning of the parameter.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
The spatiotemporal correlation factor may be represented by a floating point number of (0, 1]. A larger value of the spatiotemporal correlation factor indicates a stronger degree of time and space comprehensive correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
The attribute correlation factor may be represented by a floating point number of (0, 1]. A larger value of the attribute correlation factor indicates a stronger degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
In the foregoing implementation, indexing based on an identifier of an event can improve event query efficiency.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the method further includes: obtaining second information, where the second information indicates a current status of the first event; determining, based on the second information, that the first event changes; determining, based on the first information, that the first event meets a preset condition; and updating description information of the second event in the map based on the second information.
In the foregoing implementation, when it is detected that an event changes and meets the preset condition, quick indexing of another event associated with the event can be implemented based on the first information. This improves event searching efficiency and inter-event linkage update efficiency.
Optionally, the preset condition includes at least one of the following: the first event is a cause event of the second event; the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold; and there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold.
Optionally, the determining that the first event changes includes: determining that the first event disappears; determining that the first event is a new event; or determining that a degree of change of the first event is greater than a third threshold.
In the foregoing implementation, the preset condition is a trigger condition used to update the description information of the second event.
The condition that the first event is a cause event of the second event indicates that the second event is affected by the first event.
Compared with the former condition, the condition that the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold adds a limitation on the degree of correlation between the two events. The degree of correlation between the first event and the second event may be measured by at least one of the association factor, the spatiotemporal correlation factor, the time correlation factor, the spatial correlation factor, the attribute correlation factor, and the like.
In the condition that there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold, that there is an association relationship between the first event and the second event means that a cause and effect relationship between the first event and the second event cannot be determined, but there is an association relationship between the first event and the second event, and the association relationship may be time correlation, spatial correlation, or spatiotemporal correlation. The association relationship may alternatively be, for example, the first event and the second event occur concurrently, or the first event and the second event disappear concurrently, or the first event aggravates or reduces a degree of the second event.
For example, that the degree of correlation between the first event and the second event is greater than a second threshold may be that at least one of the association factor, the spatiotemporal correlation factor, the time correlation factor, the spatial correlation factor, the attribute correlation factor, and the like between the first event and the second event is greater than the second threshold. Alternatively, the overlapping duration between the time range of the first event and the time range of the second event may be greater than the second threshold, or the overlapping area between the location data of the first event and the location data of the second event is greater than the second threshold. This is not specifically limited in this embodiment of this application.
The determining that a degree of change of the first event is greater than a third threshold may be: The second information includes location data and attribute data of the first event at a current moment, and a value of change of the location data of the first event at the current moment compared with location data of the first event at a historical moment and/or a value of change of the attribute data of the first event at the current moment compared with attribute data of the first event at a historical moment are/is greater than the third threshold.
Optionally, when the degree of change of the first event is greater than the third threshold, the first information is updated based on the second information and updated description information of the second event in the map. Alternatively, the first information is deleted when the first event disappears.
Optionally, the method further includes: updating the first information when the following condition is met: a quantity of changed events in a plurality of events exceeds a fourth threshold; a quantity of disappeared events in a plurality of events exceeds a fifth threshold; the degree of change of the first event exceeds a sixth threshold; or a quantity of new events exceeds a seventh threshold.
In the foregoing implementation, update of description information of an event and update of association information (that is, the first information) between events may be in a dependency sequence, or may be independent and decoupled from each other. Being in a dependency sequence means that the first information is updated after the description information of the event is updated first. Being independent and decoupled from each other means that when the plurality of events meet any one of the foregoing conditions, the update of the first information may be triggered, and the update of the first information does not need to be performed after the update of the description information of the event is completed. This can improve map update efficiency.
According to a second aspect, this application provides a map using method. The method includes: obtaining first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and performing a sensed information fusion operation, an event prediction operation, or a map tile update operation based on the first information.
In the foregoing implementation, map information indicating association information between events is obtained. A map user can predict or verify, based on the map information, an event that needs to be referred to in driving decision-making. This helps improve accuracy of driving decision-making. The map user can further preferentially update or only update, based on the association relationship between the events, a tile including an event associated with a changed event. This improves map update efficiency.
It should be noted that, for beneficial effects of the content included in the first information in the second aspect, refer to descriptions of beneficial effects of the corresponding content in the first aspect. Details are not described herein again.
Optionally, the obtaining first information is specifically generating the first information or receiving the first information.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the performing a sensed information fusion operation based on the first information is specifically determining, based on the first information, a trustworthiness degree of information sensed by a sensing device.
For example, the first event is a cause event of the second event. When it is determined that a first change occurs on the first event, it may be inferred based on the first information that a second change occurs on the second event. In this case, if the sensing device senses that a third change occurs on the second event, a trustworthiness degree of the third change may be determined based on the second change.
In the foregoing implementation, a trustworthiness degree of road condition information obtained during driving can be effectively verified with reference to the first information. This helps improve accuracy of driving decision-making.
Optionally, the performing an event prediction operation based on the first information is specifically predicting a status of the second event at a preset future moment based on the first information, a current status of the first event, and a current status of the second event.
In the foregoing implementation, a status of an event associated with any event in the map may be predicted with reference to the first information, and the first information may be used as autonomous driving assistance of the map using apparatus. This helps improve accuracy of driving decision-making.
Optionally, the performing a map tile update operation based on the first information is specifically: when it is detected that the first event changes, determining, based on the first information, that the second event changes; determining a target tile to which the second event belongs in the map; and updating the target tile.
In the foregoing implementation, when it is detected that an event changes, a map tile in which an event associated with the event is located may be quickly indexed with reference to the first information, to update the map tile. This can effectively improve map update efficiency, and helps reduce network transmission pressure.
According to a third aspect, this application provides a map generation apparatus. The apparatus includes: a generation unit, configured to generate first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and a storage unit, configured to store the first information as data of the map.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the apparatus further includes: an obtaining unit, configured to obtain second information, where the second information indicates a current status of the first event; and a processing unit, configured to: determine, based on the second information, that the first event changes; determine, based on the first information, that the first event meets a preset condition; and update description information of the second event in the map based on the second information.
Optionally, the preset condition includes at least one of the following: the first event is a cause event of the second event; the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold; and there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold. The processing unit is specifically configured to: determine that the first event disappears; determine that the first event is a new event; or determine that a degree of change of the first event is greater than a third threshold.
Optionally, the method further includes: The processing unit is further configured to update the first information when the following condition is met: a quantity of changed events in a plurality of events exceeds a fourth threshold; a quantity of disappeared events in a plurality of events exceeds a fifth threshold; the degree of change of the first event exceeds a sixth threshold; or a quantity of new events exceeds a seventh threshold.
Optionally, when the degree of change of the first event is greater than the third threshold, the processing unit is specifically configured to: update the first information based on the second information and updated description information of the second event in the map; or delete the first information when the first event disappears.
Optionally, the second information includes location data and attribute data of the first event at a current moment, and the degree of change of the first event is: a value of change of the location data of the first event at the current moment compared with location data of the first event at a historical moment and/or a value of change of the attribute data of the first event at the current moment compared with attribute data of the first event at a historical moment.
Optionally, the first event and the second event may alternatively be time-related, including any one of the following: the first time range of the first event intersects with the second time range of the second event, the time correlation factor between the first event and the second event is greater than an eighth threshold; or the spatiotemporal correlation factor between the first event and the second event is greater than a ninth threshold.
Optionally, the first event and the second event may alternatively be space-related, including any one of the following: the spatiotemporal correlation factor between the first event and the second event is greater than a ninth threshold; the spatial correlation factor between the first event and the second event is greater than a tenth threshold; the first location data of the first event overlaps with the second location data of the second event; or the interval distance between the first location data and the second location data is less than an eleventh threshold.
According to a fourth aspect, this application provides a map using apparatus. The apparatus includes: an obtaining unit, configured to obtain first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and a processing unit, configured to perform a sensed information fusion operation, an event prediction operation, or a map tile update operation based on the first information.
Optionally, the obtaining unit is specifically configured to generate the first information or receive the first information.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the processing unit is specifically configured to determine, based on the first information, a trustworthiness degree of information sensed by a sensing device.
Optionally, the processing unit is specifically configured to predict a status of the second event at a preset future moment based on the first information, a current status of the first event, and a current status of the second event.
Optionally, the processing unit is specifically configured to: when it is detected that the first event changes, determine, based on the first information, that the second event changes; determine a target tile to which the second event belongs in the map; and update the target tile.
According to a fifth aspect, this application provides a map generation apparatus. The apparatus includes a processor and a memory. The memory is configured to store program instructions, and the processor invokes the program instructions in the memory, to perform the method according to any one of the first aspect or the possible implementations of the first aspect. Generally, a map is generated by a server, and the map generation apparatus may be a map server, or may be a component or chip in a map server. In addition, the map may alternatively be generated by a roadside device, a vehicle, or a mobile terminal, and the map generation apparatus may alternatively be the roadside device, the vehicle, or the mobile terminal, or a component or chip in the roadside device, the vehicle, or the mobile terminal.
According to a sixth aspect, this application provides a map using apparatus. The apparatus includes a processor and a memory. The memory is configured to store program instructions, and the processor invokes the program instructions in the memory, to perform the method according to any one of the second aspect or the possible implementations of the second aspect. The map using apparatus may be a vehicle, or may be a component (for example, a navigation apparatus or an autonomous driving apparatus in a vehicle) that can be used in a vehicle, or may be a chip that can be used in a vehicle. The map using apparatus may be a portable terminal, for example, a mobile phone, a portable computer, or a navigator, or may be a component that can be used in a portable terminal, or may be a chip that can be used in a portable terminal.
According to a seventh aspect, this application provides a map. The map includes map information, the map information indicates that there is an association relationship between a first event and a second event, the map information includes association type information, the association type information indicates a type of the association relationship, and the first event and the second event are events in the map.
The map is a map product, and may be specifically a map data product that carries map information, for example, a map update data packet; or may be a map application product that loads map information, for example, a map application that can be installed on a vehicle or a portable terminal; or may be a map display product that presents map information in a form of a graph and/or text, for example, a paper map or an electronic navigator.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, the map information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the map information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
Optionally, the map information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
Optionally, the map information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
Optionally, the map information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
Optionally, the map information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
Optionally, the map information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
Optionally, the map information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the map information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
According to an eighth aspect, this application provides a computer-readable storage medium. The computer-readable storage medium is configured to store the map according to any one of the seventh aspect or the possible implementations of the seventh aspect. The map includes map information, the map information indicates that there is an association relationship between a first event and a second event, the map information includes association type information, the association type information indicates a type of the association relationship, and the first event and the second event are events in the map.
According to a ninth aspect, this application provides a computer-readable storage medium, including computer instructions. When the computer instructions are run by a processor, the method according to any one of the first aspect or the possible implementations of the first aspect is implemented.
According to a tenth aspect, this application provides a computer-readable storage medium, including computer instructions. When the computer instructions are run by a processor, the method according to any one of the second aspect or the possible implementations of the second aspect is implemented.
According to an eleventh aspect, this application provides a vehicle, including the map using apparatus according to the fourth aspect or the sixth aspect, or including the map using apparatus according to any possible implementation of the fourth aspect or the sixth aspect.
According to a twelfth aspect, this application provides a map system, including the map generation apparatus according to any one of the third aspect, the fifth aspect, the possible implementations of the third aspect, or the possible implementations of the fifth aspect, and the map using apparatus according to any one of the fourth aspect, the sixth aspect, the possible implementations of the fourth aspect, or the possible implementations of the sixth aspect.
According to a thirteenth aspect, this application provides a computer program product. When the computer program product is executed by a processor, the method according to any one of the first aspect or the possible implementations of the first aspect is implemented. For example, the computer program product may be a software installation package. When the method provided in any possible design of the first aspect needs to be used, the computer program product may be downloaded and executed on the processor, to implement the method according to any one of the first aspect or the possible implementations of the first aspect.
According to a fourteenth aspect, this application provides a computer program product. When the computer program product is executed by a processor, the method according to any one of the second aspect or the possible implementations of the second aspect is implemented. For example, the computer program product may be a software installation package. When the method provided in any possible design of the second aspect needs to be used, the computer program product may be downloaded and executed on the processor, to implement the method according to any one of the second aspect or the possible implementations of the second aspect.
According to a fifteenth aspect, this application provides a map data processing method. The method includes: obtaining first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and storing the first information as data of the map.
In the foregoing method, map information including an association relationship between events in the map is generated. A change of another event caused by a change of an event may be predicted based on the prior information, and a prediction result may be used as a corroboration reference of the change of the another event. This helps quickly and accurately complete update of a large quantity of events in the map, and improves map update efficiency and reliability of driving decision-making.
Optionally, the obtaining first information may be specifically generating the first information or receiving the first information.
When the obtaining first information is generating the first information, the method may be executed by a map server, a component or chip in a map server, a roadside device, a vehicle, or a mobile terminal, or a component or chip in a roadside device, a vehicle, or a mobile terminal. When the obtaining first information is receiving the first information, the method may be executed by a vehicle, a component (for example, a navigation apparatus or an autonomous driving apparatus in a vehicle) in a vehicle, a chip in a vehicle, a portable terminal (for example, a mobile phone, a portable computer, or a navigator), a component in a portable terminal, a chip in a portable terminal, or the like, or may be an application that can be installed and run on a vehicle or a portable terminal.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
In the foregoing implementation, the first event and the second event are causally related. If the first event is a cause event of the second event, it indicates that the second event is affected by the first event. If the first event is a result event of the second event, that is, the second event is a cause event of the first event, it indicates that the first event is affected by the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
The association factor may be represented by a floating point number of (0, 1]. A larger value of the association factor indicates a stronger degree of correlation between the first event and the second event. The association factor is a comprehensive index, and may represent the degree of correlation between the first event and the second event overall (including a dimension such as time, space, or attribute).
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
The time correlation factor may be represented by a floating point number of (0, 1]. A larger value of the time correlation factor indicates a stronger degree of time correlation between the first event and the second event.
The degree of time correlation between the first event and the second event may be represented as overlapping duration between a time range of the first event and a time range of the second event, or may be represented as an intersection over union or a time interval between the two time ranges. Therefore, optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
In the foregoing implementation, the overlapping duration, the first intersection over union, and the time interval may all be used to measure the degree of time correlation between the first event and the second event. For example, the time correlation factor may be obtained by performing weighted summation on the overlapping duration, the first intersection over union, and the time interval. When other parameters remain unchanged, larger overlapping duration indicates a larger time correlation factor; when other parameters remain unchanged, a larger first intersection over union indicates a larger time correlation factor; and when other parameters remain unchanged, a smaller time interval indicates a larger time correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the time correlation factor, refer to a physical meaning of the parameter.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
The spatial correlation factor may be represented by a floating point number of (0, 1]. A larger value of the spatial correlation factor indicates a stronger degree of spatial correlation between the first event and the second event.
The degree of spatial correlation between the first event and the second event may be represented as an overlapping area, an interval distance, or an intersection over union between location data covered by the first event and location data covered by the second event, or may be represented as a degree of traffic reachability between the two pieces of location data. Therefore, optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
In the foregoing implementation, the overlapping area, the second intersection over union, the interval distance, and the degree of traffic reachability may all be used to measure the degree of spatial correlation between the first event and the second event. For example, the spatial correlation factor may be obtained by performing weighted summation on the overlapping area, the second intersection over union, the interval distance, and the degree of traffic reachability. When other parameters remain unchanged, a larger overlapping area indicates a larger spatial correlation factor; when other parameters remain unchanged, a larger second intersection over union indicates a larger spatial correlation factor; when other parameters remain unchanged, a smaller interval distance indicates a larger spatial correlation factor; and when other parameters remain unchanged, a larger degree of traffic reachability indicates a larger spatial correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the spatial correlation factor, refer to a physical meaning of the parameter.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
The spatiotemporal correlation factor may be represented by a floating point number of (0, 1]. A larger value of the spatiotemporal correlation factor indicates a stronger degree of time and space comprehensive correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
The attribute correlation factor may be represented by a floating point number of (0, 1]. A larger value of the attribute correlation factor indicates a stronger degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
In the foregoing implementation, indexing based on an identifier of an event can improve event query efficiency.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the method further includes: obtaining second information, where the second information indicates a current status of the first event; determining, based on the second information, that the first event changes; determining, based on the first information, that the first event meets a preset condition; and updating description information of the second event in the map based on the second information.
In the foregoing implementation, when it is detected that an event changes and meets the preset condition, quick indexing of another event associated with the event can be implemented based on the first information. This improves event searching efficiency and inter-event linkage update efficiency.
Optionally, the preset condition includes at least one of the following: the first event is a cause event of the second event; the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold; and there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold.
Optionally, the determining that the first event changes includes: determining that the first event disappears; determining that the first event is a new event; or determining that a degree of change of the first event is greater than a third threshold.
In the foregoing implementation, the preset condition is a trigger condition used to update the description information of the second event.
The condition that the first event is a cause event of the second event indicates that the second event is affected by the first event.
Compared with the former condition, the condition that the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold adds a limitation on the degree of correlation between the two events. The degree of correlation between the first event and the second event may be measured by at least one of the association factor, the spatiotemporal correlation factor, the time correlation factor, the spatial correlation factor, the attribute correlation factor, and the like.
In the condition that there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold, that there is an association relationship between the first event and the second event means that a cause and effect relationship between the first event and the second event cannot be determined, but there is an association relationship between the first event and the second event, and the association relationship may be time correlation, spatial correlation, or spatiotemporal correlation. The association relationship may alternatively be, for example, the first event and the second event occur concurrently, or the first event and the second event disappear concurrently, or the first event aggravates or reduces a degree of the second event.
For example, that the degree of correlation between the first event and the second event is greater than a second threshold may be that at least one of the association factor, the spatiotemporal correlation factor, the time correlation factor, the spatial correlation factor, the attribute correlation factor, and the like between the first event and the second event is greater than the second threshold. Alternatively, the overlapping duration between the time range of the first event and the time range of the second event may be greater than the second threshold, or the overlapping area between the location data of the first event and the location data of the second event is greater than the second threshold. This is not specifically limited in this embodiment of this application.
The determining that a degree of change of the first event is greater than a third threshold may be: The second information includes location data and attribute data of the first event at a current moment, and a value of change of the location data of the first event at the current moment compared with location data of the first event at a historical moment and/or a value of change of the attribute data of the first event at the current moment compared with attribute data of the first event at a historical moment are/is greater than the third threshold.
Optionally, the method further includes updating the first information when the following condition is met: a quantity of changed events in a plurality of events exceeds a fourth threshold; a quantity of disappeared events in a plurality of events exceeds a fifth threshold; the degree of change of the first event exceeds a sixth threshold; or a quantity of new events exceeds a seventh threshold.
In the foregoing implementation, update of description information of an event and update of association information (that is, the first information) between events may be in a dependency sequence, or may be independent and decoupled from each other. Being in a dependency sequence means that the first information is updated after the description information of the event is updated first. Being independent and decoupled from each other means that when the plurality of events meet any one of the foregoing conditions, the update of the first information may be triggered, and the update of the first information does not need to be performed after the update of the description information of the event is completed. This can improve map update efficiency.
Optionally, the method further includes determining, based on the first information, a trustworthiness degree of information sensed by a sensing device.
For example, the first event is a cause event of the second event. When it is determined that a first change occurs on the first event, it may be inferred based on the first information that a second change occurs on the second event. In this case, if the sensing device senses that a third change occurs on the second event, a trustworthiness degree of the third change may be determined based on the second change.
In the foregoing implementation, a change of another event caused by a change of an event may be predicted based on the first information, and a prediction result may be used as a corroboration reference of the change of the another event. This effectively verifies a trustworthiness degree of road condition information obtained during driving, and helps improve accuracy of driving decision-making.
Optionally, the method further includes predicting a status of the second event at a preset future moment based on the first information, a current status of the first event, and a current status of the second event.
In the foregoing implementation, a change of another event caused by a change of an event may be predicted, and the first information may be used as assistance information of autonomous driving. This helps improve accuracy of driving decision-making.
Optionally, the method further includes: when it is detected that the first event changes, determining, based on the first information, that the second event changes; determining a target tile to which the second event belongs in the map; and updating the target tile.
In the foregoing implementation, when it is detected that an event changes, a map tile in which an event associated with the event is located may be quickly indexed with reference to the first information, to update the map tile. This effectively improves map update efficiency, and helps reduce network transmission pressure.
According to a sixteenth aspect, this application provides a map data processing apparatus. The apparatus includes: a first obtaining unit, configured to obtain first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map; and a storage unit, configured to store the first information as data of the map.
Optionally, the obtaining unit is specifically configured to generate the first information or receive the first information.
Optionally, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
Optionally, the first information further includes an attribute correlation factor, and the attribute correlation factor indicates a degree of attribute correlation between the first event and the second event.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
Optionally, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
Optionally, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
Optionally, the apparatus further includes: a second obtaining unit, configured to obtain second information, where the second information indicates a current status of the first event; and a processing unit, configured to: determine, based on the second information, that the first event changes. The processing unit is further configured to determine, based on the first information, that the first event meets a preset condition. The processing unit is further configured to update description information of the second event in the map based on the second information.
Optionally, the preset condition includes at least one of the following: the first event is a cause event of the second event; the first event is a cause event of the second event, and the degree of correlation between the first event and the second event is greater than a first threshold; and there is an association relationship between the first event and the second event, and the degree of correlation between the first event and the second event is greater than a second threshold.
The processing unit is specifically configured to: determine that the first event disappears; determine that the first event is a new event; or determine that a degree of change of the first event is greater than a third threshold.
The determining that a degree of change of the first event is greater than a third threshold may be: The second information includes location data and attribute data of the first event at a current moment, and a value of change of the location data of the first event at the current moment compared with location data of the first event at a historical moment and/or a value of change of the attribute data of the first event at the current moment compared with attribute data of the first event at a historical moment are/is greater than the third threshold.
Optionally, the processing unit is further configured to update the first information when the following condition is met: a quantity of changed events in a plurality of events exceeds a fourth threshold; a quantity of disappeared events in a plurality of events exceeds a fifth threshold; the degree of change of the first event exceeds a sixth threshold; or a quantity of new events exceeds a seventh threshold.
Optionally, the processing unit is further configured to determine, based on the first information, a trustworthiness degree of information sensed by a sensing device.
Optionally, the processing unit is further configured to predict a status of the second event at a preset future moment based on the first information, the current status of the first event, and a current status of the second event.
Optionally, the processing unit is further configured to: when it is detected that the first event changes, determine, based on the first information, that the second event changes; determine a target tile to which the second event belongs in the map; and update the target tile.
According to a seventeenth aspect, this application provides a map data processing apparatus. The apparatus includes a processor and a memory. The memory is configured to store program instructions, and the processor invokes the program instructions in the memory, to perform the method according to any one of the fifteenth aspect or the possible implementations of the fifteenth aspect. The map data processing apparatus may be a map server, a component or chip in a map server, a roadside device, a vehicle, a portable terminal, or a component or chip in a roadside device, a vehicle, or a portable terminal.
According to an eighteenth aspect, this application provides a computer-readable storage medium, including computer instructions. When the computer instructions are run by a processor, the method according to any one of the fifteenth aspect or the possible implementations of the fifteenth aspect is implemented.
According to a nineteenth aspect, this application provides a computer program product. When the computer program product is executed by a processor, the method according to any one of the fifteenth aspect or the possible implementations of the fifteenth aspect is implemented. For example, the computer program product may be a software installation package. When the method provided in any possible design of the fifteenth aspect needs to be used, the computer program product may be downloaded and executed on the processor, to implement the method according to any one of the fifteenth aspect or the possible implementations of the fifteenth aspect.
According to a twentieth aspect, this application provides a vehicle, including the map data processing apparatus according to the sixteenth aspect or the seventeenth aspect, or including the map data processing apparatus according to any possible implementation of the sixteenth aspect or the seventeenth aspect.
According to a twenty-first aspect, this application provides a map data processing system. The map data processing system includes a first map data processing apparatus and a second map data processing apparatus. The first map data processing apparatus is configured to perform the method according to any one of the first aspect or the possible implementations of the first aspect. The second map data processing apparatus is configured to perform the method according to any one of the second aspect or the possible implementations of the second aspect.
Terms used in embodiments of this application are merely for a purpose of illustrating specific embodiments, and are not intended to limit this application. Terms “first”, “second”, and the like are used to distinguish between different description objects, but are not used to limit an order of a plurality of description objects or limit a quantity of the description objects, and are not used to distinguish between different entity objects. For example, “first information” is not necessarily prior to “second information”. The “first information” may be one piece of information, or may be a plurality of pieces of information. In reality, the “first information” and the “second information” may be same information.
A high definition map includes a plurality of layers. A layer is a map data set with a specific organization structure. Data at a layer is organized in a specific data structure, and can describe information elements from a plurality of sources. Based on time variability of the information elements, the information elements may be classified into two types: elements and events. An element is an information element that is fixed, with small changes, or with a long update periodicity, for example, a road topology, a building location, a lane line, a lane direction, or a traffic infrastructure layout. An event is an information element with strong time-varying characteristic, for example, a traffic accident, a weather change, a road construction, or traffic congestion. In a map, elements and events are recorded at different layers. Information about the elements is carried at a static layer in the map, and information about the events is carried at a dynamic layer in the map. For example, the high definition map includes one static layer and a plurality of dynamic layers. The static layer describes geographical distribution of buildings, roads, traffic lights, and road signs. A dynamic layer 1 describes a real-time speed limit situation, a traffic construction situation, and a passenger and vehicle flow situation of a lane. A dynamic layer 2 describes a weather change, for example, raining, snowing, or wind blowing. It should be noted that, a map description object may have both a time-varying information element and an information element that basically does not change with time, that is, the description object is related to both an element in a map and an event in the map. For example, for a lane, a geographical location of the lane is an element in the map, and a traffic flow of the lane is an event in the map; and for a traffic light, an intersection at which the traffic light is located is an element in the map, and a light-on change of the traffic light is an event in the map.
Static information may specifically include road network structure data, road traffic facility data (for example, a traffic sign or a roadside facility), and the like. The road network structure data may be classified into a region level, a road level, and a lane level. Each region has a unique region identification (ID), and the region ID may also be referred to as a geographical region ID, and a tile ID is generally used. Each region includes a plurality of roads, and each road has a unique road ID. Each road includes a plurality of lanes, and each lane has a unique lane ID. In addition, there is a road topology connection relationship between regions, between roads, and between lanes. It should be noted that a road and a lane may be referred to as map elements in the high definition map.
A tile may be understood as follows: A map within a specific range is cut into rectangular raster pictures of several rows and columns based on a specific size and format and different map resolutions, and the rectangular raster picture obtained through cutting is referred to as a tile. A higher resolution of the map indicates a larger quantity of times of cutting, a larger quantity of tiles included in the map, and higher level of tiles. When a cutting mode is cross cutting, a tile of a specific level includes four tiles of a corresponding higher level.
For example, a tile 1 is a tile of a specific level in the map, and four tiles of a level higher than that of the tile 1 are further generated by performing cross-cutting on the tile 1, respectively identified as 1-00, 1-01, 1-10, and 1-11. It may be understood that geographical coverage of the tile 1 is a union set of geographical coverage of the tile 1-00, geographical coverage of the tile 1-01, geographical coverage of the tile 1-10, and geographical coverage of the tile 1-11.
Dynamic information describes an event, for example, a road construction event, a road maintenance event, a snowstorm cold event, a rainstorm event, a traffic accident event, a traffic congestion event, a temporary speed limit event, a lane topology event, or a road surface icing event. In addition, there is an association relationship between the events. For example, the road construction event affects the traffic congestion event, and the road surface icing event causes the traffic accident event.
When an event changes, the map needs to be updated in time to ensure accuracy of information provided by the map. Because a status of the event changes quickly, the dynamic information in the map needs to be updated in real time.
Richness of information in an existing map cannot meet a use requirement. Therefore, embodiments of this application provide a map data processing method, so that a map includes an association relationship between events. Therefore, when a changed event is detected, an associated event affected by the changed event and associated update of the associated event can be quickly determined based on the association relationship between the events. This improves update efficiency of the event in the map, ensures accuracy and reliability of dynamic information provided by the map, and helps improve accuracy of a driving decision made by a vehicle.
The following describes technical solutions of this application with reference to accompanying drawings.
The data source device is configured to provide the computing device with basic data including an event (the data source device and the computing device may be a same device). The computing device obtains basic event information based on the basic data provided by the data source device, where the basic event information includes descriptions of time and a status of each event in the map; and then generates event association information based on the basic event information, where the event association information indicates an association relationship between events in the map. In some possible embodiments, when detecting, based on real-time data provided by the data source device, that an event changes, the computing device may update, based on a current status of the event, description information of an associated event affected by the event and association information between the event and the associated event. It should be noted that the map may be a high definition map, or another type of map including a region, a road, and a lane. This is not specifically limited in this embodiment of this application.
The data source device includes but is not limited to a terminal, a roadside apparatus, and the like. The roadside apparatus is configured to obtain road information, traffic condition information, and the like of wide location data. The roadside apparatus may include a roadside unit (RSU), a multi-access edge computing (MEC) apparatus, a sensor, or the like, for example, may be an RSU, MEC, or a sensor, or may be a system including an RSU and MEC, or may be a system including an RSU and a sensor, or may be a system including an RSU, MEC, and a sensor. The terminal may obtain road information, traffic condition information, and the like by using a sensor of the terminal. The terminal may be a vehicle, an on board unit (OBU), an intelligent wearable device (for example, a sports band or a watch), or another sensor or device that can communicate with the computing device, such as a portable mobile device (for example, a mobile phone or a tablet) or a component or chip of a portable mobile device. This is not specifically limited in this embodiment of this application. In some possible embodiments, the data source device may alternatively be a device used by a traffic management department to provide traffic road condition data, or the like.
The map may be generated by the computing device, and the computing device may be a device having a computing function, for example, a computer, a server, a component or chip in a server, or multi-access edge computing (MEC). It should be noted that the computing device may be a cloud computing server, or may be an edge computing server. The computing device may be an integrated device, or may be a plurality of distributed devices. This is not specifically limited in this embodiment of this application.
The map may be released by using a mobile communication network or a V2X (Vehicle to Everything, Internet of Vehicles). For example, a map server on the cloud may release updated map information to a portable terminal handheld by a pedestrian by using a mobile communication network including a base station, or may forward the updated map information to the portable terminal by using a roadside device by using V2X communication. To meet a real-time requirement, update frequency of the high definition map is calculated by day, hour, or minute.
Because both the roadside device and the terminal device have an information collection capability and a computing capability, the roadside device and the terminal device may not only serve as a map receiver and a user, but also serve as a map producer to locally generate the map information for use by the roadside device and the terminal device, or to send the map information to another roadside device or terminal device. In this way, the roadside device and the terminal device are both data source devices and computing devices.
The computing device in
It should be noted that, in the foregoing systems, communication between data source devices, and between a data source device and a computing device or a terminal may use a cellular communication technology, for example, 2G cellular communication, such as a global system for mobile communication (GSM), or general packet radio service (GPRS); 3G cellular communication, such as wideband code division multiple access (WCDMA), time division-synchronous code division multiple access (TS-SCDMA), or code division multiple access (CDMA); 4G cellular communication, such as long term evolution (LTE); 5G cellular communication; or another evolved cellular communication technology. A wireless communication system may alternatively communicate with a wireless local area network (WLAN) by using a non-cellular communication technology, for example, Wi-Fi. In some embodiments, direct communication between the foregoing devices may also be performed by using an infrared link, Bluetooth, or ZigBee. In some embodiments, communication between the foregoing devices may alternatively use another wireless protocol, for example, various vehicle communication systems. For example, the system may include one or more dedicated short range communications (DSRC) devices. These devices may include public and/or private data communication between vehicles and/or roadside stations. This is not specifically limited in this application.
It should be noted that
In this embodiment of this application, an event is an object that changes with time and lasts for specific duration in the map. For example, the event may be a traffic accident event, a traffic congestion event, a road maintenance event, a road surface icing event, a road surface collapse event, a traffic prohibition event, a temporary speed limit event, a temporary lane topology connection event, a snowstorm cold event, a rainstorm event, or the like.
The basic event information includes description information of a plurality of events in the map.
The event identifier (ID) is used to uniquely identify an event in the map, and may be a combination of one or more characters. The character may be one or more of a number, a letter, and another symbol, for example, a combination of one or more numbers, or a combination of one or more data and letters.
The time information may be a field used to indicate a year, a month, a day, an hour, or a minute, and indicates start time, end time, or duration of the event; or may be a timestamp as shown in
The event information may include location data and attribute data.
The location data of the event indicates a location at which the event occurs. For example, the location data may include one or more of an ID of a tile in which the event is located, an ID of a bound map element, a geographical partition ID, longitude and latitude information, absolute coordinates, or relative coordinates. The location data may further represent location data covered by the event. In this embodiment of this application, the location data of the event may be further represented in another manner. In a specific implementation, when the location data of the event is represented as a regular shape, the location data of the event may be represented by using one or more parameters, such as a distance or coordinates, relative to a reference point (for example, a start point of a lane or a road). For example, the location data of the event is a section of road, and the reference point is a start point of the road. In this case, (120 m, 140 m) means starting at a position 120 m away from the reference point and ending at a position 140 m away from the reference point. Alternatively, the location data of the event is represented by using geographical coordinates of two end points of the road. In another specific implementation, when the location data of the event is represented as an irregular shape, the location data of the event may be represented by geographical coordinates of a plurality of corner points of the irregular shape.
The attribute data of the event indicates a feature or a status of the event. In this embodiment of this application, different events have different attributes. First, the attribute data may include a type code of the event. For example, a road surface icing event is indicated with 0001, a traffic accident event is indicated with 0002, a rainstorm event is indicated with 0003, and a traffic congestion event is indicated with 0004. Further, different types of events may have attribute data indicating different content. For example, attributes of the road surface icing event include an ice thickness, an ice surface friction coefficient, and the like; attributes of the traffic accident event include an accident type, a motion status parameter of the vehicle when the accident occurs, a model of the vehicle on which the accident occurs, and the like; attributes of the rainstorm event include rainfall, a warning level, a wind speed, a wind direction, and the like; and attributes of the traffic congestion event include a congestion length, a congestion time period type, a congestion degree, and the like. This is not specifically limited in this embodiment of this application.
A specific example is used to describe the description information of the event.
For example, description information of a traffic congestion event A includes that an event identifier is A and a timestamp is a moment 1, indicating that the traffic congestion event A occurs at the moment 1. Location data of the traffic congestion event A includes a tile 1, a lane 1, and (100 m, 400 m), indicating that the traffic congestion event A occurs on the lane 1 in a region 1, and the location data starts at a position 100 m away from start point coordinates of the lane 1 and ends at a position 400 m away from the start point coordinates of the lane 1. Attribute data of the traffic congestion event A includes a congestion length, a congestion time period type, a congestion degree, and the like.
In this embodiment of this application, the ID of the bound map element of the event may be a lane ID, a road ID, or the like. It should be noted that a quantity of IDs of bound map elements of the event is not specifically limited in this embodiment of this application, and the quantity of IDs of bound map elements of the event is related to the location data occupied by the event.
In this embodiment of this application, because the event dynamically changes, the description information of the event may also include event information of the event at a plurality of moments.
It may be understood that an event ID of a same event at different timestamps remains unchanged, but the same event may have different states at the different timestamps. For example, at least one of location data and attribute data of the event changes with time. Event information of each event at different time may be recorded. For example, event information of each event at different time may be stored in a basic event information base, so that different index conditions may be set based on a requirement to obtain corresponding event information. For example, event information of an event at different time may be indexed based on an event ID, or one or more events that occur at specific time or in a specific time period may be indexed based on time information, or event information of the event at specific time or in a specific time period may be indexed based on the event ID and the time information.
In this embodiment of this application, if event description information of an event has a plurality of timestamps, the plurality of timestamps may form a time range of the event. For example, if the description information of the event i includes three timestamps: a timestamp 1 (9:00 AM), a timestamp 2 (9:10 AM), and a timestamp 3 (9:20 AM), a time range of the event i is 9:00 AM to 9:20 AM.
In this embodiment of this application, description information of each event in the map may be stored based on a requirement and a time period, for example, may be stored by day or hour as a timestamp, to save storage space.
The event association information may be generated based on the basic event information, and the event association information indicates an association relationship between a plurality of groups of related two events in the map. For example, the event association information indicates that there is an association relationship between a first event and a second event. In this embodiment of this application, the event association information includes association type information, and the association type information indicates a type of the association relationship.
Specifically, the type of the association relationship between the first event and the second event includes any one of the following:
The type 1 or type 2 indicates that the association relationship between the first event and the second event is a cause and effect relationship, and the type 3 indicates that the association relationship between the first event and the second event is a correlation relationship. It should be noted that the correlation relationship means that there is impact or an association between two events but no cause and effect relationship can be determined. For example, the first event and the second event occur concurrently, or the first event and the second event disappear concurrently, or the first event aggravates or reduces a degree of the second event. It should be noted that the event association information may be obtained by performing association relationship analysis and calculation based on the description information of each event in the basic event information.
For example, a cold snow day easily causes road surface icing. In this case, there is a cause and effect relationship between a snowstorm event and a road surface icing event. Specifically, the snowstorm event is a cause event of the road surface icing event, and the road surface icing event is a result event of the snowstorm event. For another example, a traffic congestion event and a temporary speed limit event are related to each other, and a road surface icing event is a related event of a traffic accident event.
In this embodiment of this application, the event association information further includes an association factor, and the association factor indicates an association degree or association strength between the first event and the second event. The association factor may be obtained by performing correlation analysis based on description information of the first event and description information of the second event. The association factor is a comprehensive index used to measure overall strength of the association relationship between the first event and the second event.
The association factor may be represented by a floating point number of (0, 1]. A larger value of the association factor indicates a stronger degree of association between the first event and the second event.
In this embodiment of this application, the event association information may further include one or more of a time range intersection parameter, a time intersection over union parameter, a time distance parameter, and the like. The time range intersection parameter indicates overlapping duration of time ranges of two events (namely, the first event and the second event). The time intersection over union parameter indicates a ratio of an intersection set (namely, the overlapping duration) of the time ranges of the two events to a union set of the time ranges of the two events. The time distance parameter indicates interval duration between the time ranges of the two events. It should be noted that, because description information of an event is stored in sequence based on timestamps, a time range of the event may be determined based on a plurality of historical timestamps (or referred to as historical moments) of the event.
For example, it is assumed that a time range of an event 1 is 9:00 AM to 9:20 AM, and a time range of an event 2 is 9:10 AM to 9:30 AM. It can be learned that the time range of the event 1 overlaps with the time range of the event 2, an intersection set of the time ranges is 9:10 AM to 9:20 AM, overlapping duration is 10 minutes, a union set of the time ranges of the two events is 9:00 AM to 9:30 AM, a time range union set parameter is 30 minutes, a time intersection over union parameter is 0.33 (10/30), and a time distance parameter is 0. It may be understood that, if the time range of the event 1 is 9:00 AM to 9:20 AM, and the time range of the event 2 is 9:30 AM to 9:40 AM, it can be learned that the time range of the event 1 does not overlap with the time range of the event 2, and the time distance parameter is 10 minutes.
It should be noted that a time correlation factor may be obtained by performing weighted sum calculation on the time range intersection parameter, the time intersection over union parameter, and the time distance parameter. Generally, when other parameters remain unchanged, a larger time range intersection parameter indicates a larger time correlation factor; when other parameters remain unchanged, a larger time intersection over union parameter indicates a larger time correlation factor; and when other parameters remain unchanged, a smaller time distance parameter indicates a larger time correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the time correlation factor, refer to a physical meaning of the parameter.
In this embodiment of this application, the event association information may further include a spatial correlation factor, and the spatial correlation factor indicates a degree of correlation between the first event and the second event in a space dimension.
In this embodiment of this application, the event association information further includes one or more of a spatial distance parameter, an overlapping region area parameter, a region intersection over union parameter, a topology connectivity parameter, and the like. The spatial distance parameter indicates an interval distance between two events (namely, the first event and the second event), the overlapping region area parameter indicates an area of an overlapping region of location data of the two events, the region intersection over union parameter indicates a ratio of an intersection set of the location data of the two events to a union set of the location data of the two events, and the topology connectivity parameter indicates a degree of traffic reachability between the location data of the two events. The topology connectivity parameter may be determined based on a quantity of map elements that implement traffic reachability of the location data of the two events and lengths of the map elements. When the quantity of map elements that implement traffic reachability of the location data of the two events is closer to 1 and the lengths of the map elements are shorter, the degree of traffic reachability between the location data of the two events is larger. In other words, when the topology connectivity parameter is smaller, the degree of traffic reachability between the location data of the two events is larger, and the degree of correlation between the two events in the space dimension is higher.
For example, the topology connectivity parameter may indicate a sum of the quantity of map elements that implement traffic reachability of the location data of the two events and a sum of the lengths of the map elements. For example, a lane 1 and a lane 2 are connected, and the lane 1 and the lane 2 are connected between location data of an event 1 and location data of an event 2. The lane 1 is connected to the lane 2. When running, a vehicle sequentially passes through the location data of the event 1, the lane 1, the lane 2, and the location data of the event 2. That is, the lane 1 and the lane 2 implement traffic reachability between the location data of the event 1 and the location data of the event 2. In this case, a formula for expressing a topology connectivity parameter between the two events may be: 2+a length of the lane 1+a length of the lane 2.
It should be noted that the spatial correlation factor may be obtained by performing weighted sum calculation on the spatial distance parameter, the overlapping region area parameter, the region intersection over union parameter, and the topology connectivity parameter. Generally, when other parameters remain unchanged, a smaller spatial distance parameter indicates a larger spatial correlation factor; when other parameters remain unchanged, a larger overlapping region area parameter indicates a larger spatial correlation factor; when other parameters remain unchanged, a larger region intersection over union parameter indicates a larger spatial correlation factor; and when other parameters remain unchanged, a smaller topology connectivity parameter indicates a larger spatial correlation factor. It may be understood that, for details about whether a parameter is positively correlated or negatively correlated with the spatial correlation factor, refer to a physical meaning of the parameter.
In this embodiment of this application, the event association information may further include a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of correlation between the first event and the second event in time and space dimensions. For example, the spatiotemporal correlation factor may be obtained by performing weighted summation on the time correlation factor and the spatial correlation factor.
In this embodiment of this application, the event association information may further include an attribute correlation factor, and the attribute correlation factor indicates a degree of correlation between the first event and the second event in an attribute dimension.
In this embodiment of this application, the event association information further includes an identifier of the first event, an identifier of the second event, an identifier of a map tile in which the first event is located, and an identifier of a map tile in which the second event is located.
The following describes two representation forms of the event association information provided in this embodiment of this application. The first form is a parameter matrix. To be specific, association information between the first event and the second event is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index. The second form is a tree structure. To be specific, the association information between the first event and the second event is stored in a data structure used to describe the first event or the second event. It should be noted that the following representation forms shown in
First Form: Parameter Matrix
It should be noted that the parameter matrix shown in
In this embodiment of this application, a form of the parameter matrix shown in
In a specific implementation, content of in
In a specific implementation, content of in
It should be noted that for specific meanings of the parameters shown in
Second Form: Tree Structure
Specifically, an event is used as a unit, and an event associated with the event is determined based on description information of each event in basic event information. Therefore, each event has an associated event list, and the associated event list is used for listing at least one event associated with the event.
For example, for an event A, if the event A is a cause event of an event B, the event A is a result event of an event C, and the event A is a related event of an event D, an associated event list of the event A includes the event B, the event C, and the event D.
In a specific implementation, the association information between the event A and the event B shown in
In a specific implementation, the association information between the event A and the event B shown in
In a specific implementation, the association information between the event A and the event B shown in
It should be noted that for specific meanings of the parameters shown in
It should be noted that association information between the event A and the event C in
In conclusion, when event association information indicates an association relationship between a plurality of groups of two associated events, association information between events may be represented in the form shown in
On a basis of establishing event association information between events, a complex association relationship between a plurality of events may be maintained in map data by using a data structure of a graph, that is, an event association graph indicating the association relationship between the plurality of events is established in the map data. The event association graph is a topology structure formed by a plurality of nodes and edges. The node indicates an event, and the edge indicates that two events corresponding to two connected nodes are associated. In the event association graph, if an edge exists between two nodes, it indicates that there is an association relationship between two events corresponding to the two nodes. If no edge exists between two nodes, it indicates that two events corresponding to the two nodes are not associated.
In a specific implementation, an edge in
In a specific implementation, in
In another specific implementation, in
In conclusion, after the basic event information and the event association information are generated, if it is detected that an event changes, an association event affected by the event may be radially indexed based on the event association information, to implement linkage update of the changed event in a map and the association event of the event. This helps improve efficiency of linkage update of the event. For a specific updating method, refer to the following description. Details are not described herein again.
Refer to
S301: Obtain first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, and the at least two events are events in a map.
In this embodiment of this application, obtaining the first information is specifically generating the first information. In this case, the method described in the embodiment in
Alternatively, in this embodiment of this application, obtaining the first information is specifically receiving the first information. In this case, the method described in the embodiment in
In this embodiment of this application, the first information includes association type information, and the association type information indicates a type of an association relationship between the at least two events, for example, a type of an association relationship between the first event and the second event.
Optionally, the first event may be an existing event in the map, that is, an event that has occurred and is ongoing.
Optionally, the first event may alternatively be a new event.
Optionally, the first event or the second event may be any one of the following: a snowstorm cold event, a rainstorm event, a traffic accident event, a traffic congestion event, a road maintenance event, a road construction event, a road surface icing event, a temporary speed limit event, or a lane topology event.
Optionally, in this embodiment of this application, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
Optionally, in this embodiment of this application, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
Optionally, the first information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event. It should be noted that the time correlation factor is the time correlation factor in the foregoing embodiment.
Optionally, the first information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range. It should be noted that, the information indicating the overlapping duration may be the time range intersection parameter in the foregoing embodiment, the information indicating the first intersection over union may be the time intersection over union parameter in the foregoing embodiment, and the information indicating the time interval may be the time distance parameter in the foregoing embodiment.
Optionally, the first information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event. It should be noted that the spatial correlation factor is the spatial correlation factor in the foregoing embodiment.
Optionally, the first information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data. It should be noted that, the information indicating the overlapping area may be the overlapping region area parameter in the foregoing embodiment, the information indicating the second intersection over union may be the region intersection over union parameter in the foregoing embodiment, the information indicating the interval distance may be the spatial distance parameter in the foregoing embodiment, and the information indicating the traffic reachability degree may be the topology connectivity parameter in the foregoing embodiment.
Optionally, the first information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event. It should be noted that the spatiotemporal correlation factor is the spatiotemporal correlation factor in the foregoing embodiment.
Optionally, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
The association information shown in
S302: Store the first information as data of the map.
In this embodiment of this application, the first information may be stored in a data structure that is in the map and that is used to describe the first event or the second event. For example, the first information may be represented by using the associated event list shown in
In this embodiment of this application, the first information may alternatively be stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index. It should be noted that the first information may be stored in the parameter matrix shown in
In some possible embodiments, when the obtaining first information is generating the first information, the method may further include: sending the first information, so that a receiving end of the first information performs operations such as sensed information fusion and map tile update based on the first information. When the obtaining first information is receiving the first information, the method may further include: performing operations such as sensed information fusion and map tile update based on the first information. For a specific process, refer to a related description of S402 in the following embodiment in
It can be learned that, in an implementation of this embodiment of this application, the map includes an association relationship between events in the map, and the association information is used as prior information, so that the map meets more abundant use requirements. For example, a change of another event caused by a change of an event may be predicted based on an association relationship between events in the map, and a prediction result may be further used as a corroboration reference of the change of the another event. This helps quickly and accurately complete update of a large quantity of events in the map, and improves map update efficiency and reliability of driving decision-making.
S101: Obtain detection information, where the detection information indicates a current status of a first event.
In this embodiment of this application, the current status of the first event may be reflected by using location data and/or attribute data that are/is updated based on the detection information and related to the first event. The first event may be an event already stored in basic event information, or may be a new event (that is, an event not stored in the basic event information). This is not specifically limited in this embodiment of this application.
The detection information may be obtained from a data source device. For the data source device, refer to the related description of the data source device in
S102: Determine, based on the detection information, whether the first event changes.
In this embodiment of this application, determining, based on the detection information, that the first event changes includes any one of the following:
In this embodiment of this application, that the first event changes means that at least one of the location data and the attribute data of the first event changes. That the degree of change of the first event is greater than the preset threshold means that a value of change of location data of the first event at a current moment compared with location data (stored in the basic event information) at a historical moment is greater than the preset threshold, and/or a value of change of attribute data of the first event at the current moment compared with attribute data (stored in the basic event information) of the first event at the historical moment is greater than the preset threshold.
For example, if the first event is a road surface icing event, and it is determined, based on the detection information, that a value of change of an “ice thickness” in attribute data of the road surface icing event exceeds the preset threshold, it indicates that a degree of change of the road surface icing event is greater than the preset threshold.
In this embodiment of this application, before it is determined, based on the detection information, that the first event changes, whether a confidence level of the detection information is greater than a preset truth value may be determined first. When the confidence level of the detection information is greater than the preset truth value, it is determined, based on the detection information, that the first event changes. The confidence level of the detection information may be directly provided by the data source device of the detection information, or may be obtained through calculation by performing data fusion or mutual check on the detection information and other related information.
In this embodiment of this application, determining that the first event changes may alternatively be: a value of change of the association factor between the first event and an associated event is greater than a preset difference threshold, that is, a value of difference between the association factor between the first event and the associated event at a current moment and an association factor at a historical moment is greater than the preset difference threshold. In some possible embodiments, the association factor may alternatively be replaced with the foregoing “time correlation factor”, “spatial correlation factor”, “attribute correlation factor”, or the like. This is not specifically limited in this embodiment of this application.
S103: Determine, based on association information and first information, whether the first event meets a preset condition.
In this embodiment of this application, the first information is the first information in the embodiment shown in
In this embodiment of this application, the first information may be obtained from the foregoing event association information, or may be obtained through calculation based on the detection information and description information of each event in the basic event information. This is not specifically limited in this embodiment of this application.
In this embodiment of this application, if it is determined, based on the first information, that the first event does not meet the preset condition, an update operation does not need to be performed. If it is determined, based on the first information, that the first event meets the preset condition, S104 is performed.
In this embodiment of this application, the preset condition includes any one of the following:
(1) The first event is a cause event of the second event.
This may be specifically learned by using the parameter “association type” in the first information.
(2) The first event is a cause event of the second event, and a degree of correlation between the first event and the second event is greater than a first threshold.
That a degree of correlation between the first event and the second event is greater than a first threshold may be that a correlation factor between the first event and the second event is greater than the first threshold.
(3) The first event is a related event of the second event, and a degree of correlation between the first event and the second event is greater than a second threshold.
That the first event is a related event of the second event means that there is an association relationship between the first event and the second event, but this correlation relationship is different from a cause and effect relationship. Specifically, the correlation relationship may be, for example, time correlation and/or spatial correlation between the first event and the second event. It should be noted that the time correlation and spatial correlation between the first event and the second event may be referred to as spatiotemporal correlation between the first event and the second event.
A manner of determining the time correlation between the first event and the second event may be as follows: A spatiotemporal correlation factor between the first event and the second event is greater than the second threshold; a time correlation factor between the first event and the second event is greater than the second threshold; overlapping duration between a time range of the first event and a time range of the second event is not zero (that is, the time range intersection parameter or the time intersection over union parameter is not 0); or interval duration (namely, the time distance parameter) between the time range of the first event and the time range of the second event is less than a preset duration threshold.
A manner of determining the spatial correlation between the first event and the second event may be as follows: A spatiotemporal correlation factor between the first event and the second event is greater than the second threshold; a spatial correlation factor between the first event and the second event is greater than the second threshold; an area of an overlapping region between location data of the first event and location data of the second event is not zero (that is, the overlapping region area parameter or the region intersection over union parameter is not 0); or an interval distance (namely, the spatial distance parameter) between the location data of the first event and the location data of the second event is less than a preset interval threshold.
It should be noted that, that a spatiotemporal correlation factor between the first event and the second event is greater than the second threshold also indicates the spatiotemporal correlation between the first event and the second event.
Therefore, that the first event is a related event of the second event, and a degree of correlation between the first event and the second event is greater than a second threshold specifically includes any one of the following:
In this embodiment of this application, the preset condition may alternatively be: the overlapping duration between the time range of the first event and the time range of the second event is not zero, or the area of the overlapping region between the location data of the first event and the location data of the second event is not zero, or the like. This is not specifically limited in this embodiment of this application.
In this embodiment of this application, the preset condition cannot be that the interval duration between the time range of the first event and the time range of the second event is less than the preset duration threshold. A specific reason is as follows: According to the foregoing definition, although the first event is time-related to the second event, there is an interval between the time range of the first event and the time range of the second event, and it indicates that the first event and the second event occur sequentially. If the first event occurs first and the first event changes, but the second event that occurs later has not occurred this time, or if the first event occurs later and the first event changes, but the second event that occurs first has ended, although the two events are time-related, a change of one event does not trigger update of the other event.
S104: When determining that the first event changes and the first event meets the preset condition, update description information of the second event in the map based on the detection information.
In this embodiment of this application, because the second event is affected by the first event, when the first event meets the preset condition, the description information of the second event in the map may be updated based on the detection information.
Specifically, updating the description information of the second event in the map based on the detection information means: obtaining historical description information of the second event from the basic event information; obtaining prediction information of the second event through prediction based on the detection information and the historical description information of the second event, where the prediction information of the second event includes location data and attribute data of a second target event at a current moment; and finally adding the prediction information of the second event to the description information of the second event, to update the description information of the second event in the map.
In some possible embodiments, if the first event does not meet the foregoing three cases in S102, it indicates that a change amplitude of the first event is small. In this case, the description information of the second event in the basic event information does not need to be updated.
In some possible embodiments, if it is determined in S102 that the first event disappears, in addition to performing S104, description information of the first event may be further deleted.
In some possible embodiments, if it is determined in S102 that the first event is a new event, in addition to performing S104, the detection information may be further added to the map as the description information of the first event.
In some possible embodiments, if it is determined in 102 that the degree of change of the first event is greater than the preset threshold, in addition to performing S104, the detection information may be further added to the description information of the first event, to update the description information of the first event.
It should be noted that update of an event may be transferred. For example, it is determined that an event A is associated with an event B based on association information of the event A, and a change of the event A triggers update of the event B, which is equivalent to that the event B changes. Description information of the updated event B may also be used as the detection information in S101. If the event B also meets the foregoing three cases in S102, update of an event associated with the event B is triggered. This process continues until there is no event affected by the changed event in the map, and the update transfer of the event stops.
It can be learned that, after a change of an event is detected, when change information of an associated event of the event is not reported in time, a latest status of the associated event can be effectively predicted based on an association relationship between the events, to implement linkage update of the event in the map, and improve event update efficiency.
The following uses a specific scenario to describe the foregoing method embodiment.
Optionally, in some possible embodiments, the following step may be further performed.
S105: Update the first information.
Specifically, the first information may be updated based on the detection information and the description information of the updated second event, and/or the first information may be updated based on the updated first event.
In this embodiment of this application, the first information indicates that there is an association relationship between the first event and the second event. For specific content included in the first information, refer to the content shown in
In this embodiment of this application, after the description information of the second event in the map is updated based on the detection information, update of the first information may be further directly triggered. Updating the first information specifically means updating at least one of the association factor, the time correlation factor, the spatial correlation factor, the spatiotemporal correlation factor, and the attribute correlation factor between the first event and the second event.
In this embodiment of this application, updating the first information further includes updating at least one of the time range intersection parameter, the time intersection over union parameter, the time distance parameter, the spatial distance parameter, the overlapping region area parameter, the region intersection over union parameter, and the topology connectivity parameter between the first event and the second event.
In this embodiment of this application, the update operation may be specifically modification, deletion, or addition.
In a specific implementation, when it is determined that the first event disappears, updating the first information means deleting the first information.
For example, in
In a specific implementation, when it is determined that a degree of change of the first event is greater than the preset threshold, updating the first information means modifying the first information.
For example, in
In a specific implementation, when it is determined that the first event is a new event, updating the first information means updating the first information and adding updated first information.
For example, in
It can be learned that, in an implementation of this embodiment of this application, after a change of an event is detected, association information between the event and another event associated with the event may be quickly updated based on an association relationship between the events.
S201: Obtain event association information.
In this embodiment of this application, the event association information may indicate an association relationship between a plurality of events in a map, including an association relationship between every two of the plurality of events. For example, the plurality of events include the first event and the second event.
It should be noted that, for details of the event association information, refer to the description about the event association information in the foregoing embodiment. Details are not described herein again.
It should be noted that basic event information further needs to be obtained. For a specific description of the basic event information, refer to the related description of the basic event information in the foregoing embodiment. Details are not described herein again.
S202: Obtain status detection information.
In this embodiment of this application, the status detection information indicates a change status of location data and/or attribute data of the plurality of events in the map.
S203: Update the event association information when determining, based on the status detection information, that the plurality of events meet a preset update condition.
The preset update condition is a trigger condition used to update the event association information. The preset update condition may be the following condition: a quantity of changed events in the plurality of events exceeds a fourth threshold; a quantity of disappeared events in the plurality of events exceeds a fifth threshold; a degree of change of an event in the plurality of events exceeds a sixth threshold; or a quantity of new events exceeds a seventh threshold.
In this embodiment of this application, the preset update condition may alternatively be that a total quantity of events in the plurality of events that meet a condition such as a disappeared event, a new event, or an event whose change degree is greater than the sixth threshold exceeds an eighth threshold. For example, it is assumed that the plurality of events include five events, which are respectively an event 1, an event 2, . . . , and an event 5. If it is detected, based on the status detection information and the basic event information, that the event 1 and the event 2 do not change, the event 3 disappears, the event 4 is a new event, and a change degree of the event 5 is greater than the sixth threshold, it is easily known that a total quantity of events in the plurality of events that meet a condition such as a disappeared event, a new event, or an event whose change degree is greater than the sixth threshold is 3.
In this embodiment of this application, when the preset update condition is met, the event association information is updated. For a specific update process of the association information between every two events in the event association information, refer to the related description of S105 in the embodiment in
It should be noted that, whether each of the plurality of events meets the preset update condition and a quantity of events that are in the plurality of events and that meet the preset update condition may be determined based on the status detection information and the basic event information.
It can be learned that update of the association information between events and update of the description information of the event may be independent of each other. Based on the association relationship between events, association information between events corresponding to changed events can be quickly updated, to improve map update efficiency.
S401: Obtain first information, where the first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map.
In this embodiment of this application, obtaining the first information is specifically receiving the first information. The first information may be sent by a map generation apparatus.
In this embodiment of this application, obtaining the first information is specifically generating the first information. For details of this step, refer to the related description of S301 in the embodiment in
In this embodiment of this application, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
In this embodiment of this application, the first information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
In this embodiment of this application, the first information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
In this embodiment of this application, the first information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
In this embodiment of this application, the first information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
It should be noted that, for details of the first information, refer to the related description of the first information in S301 in the embodiment in
S402: Perform an event prediction operation, a sensed information fusion operation, or a map tile update operation based on the first information.
In this embodiment of this application, performing the event prediction operation based on the first information may be predicting a status of the second event at a preset future moment based on the first information, a current status of the first event, and a current status of the second event.
For example, the navigation apparatus in the vehicle receives the first information, and the first information indicates that the first event (for example, a traffic accident event 1) is associated with the second event (for example, a traffic congestion event 2). It is assumed that a front-facing camera of the vehicle detects a current status of the traffic accident event 1. The vehicle predicts a status of the traffic congestion event 2 at a preset future moment based on the current status of the traffic accident event 1, a current status of the traffic congestion event 2 in the map, and the first information, to estimate congestion duration of the traffic congestion event 2. The predicted status of the second event at the preset future moment may be used to generate reminder information, or perform an operation such as path planning.
In this embodiment of this application, performing the sensed information fusion operation based on the first information may be determining, based on the first information, a trustworthiness degree of information sensed by a sensing device.
For example, the sensing device includes but is not limited to a camera of a vehicle, a laser radar, a millimeter wave radar, and the like. The camera of the vehicle is used as an example. It is assumed that during running of the vehicle, after a front-facing camera of the vehicle performs processing and analysis based on a photographed image, it is determined that a traffic accident event occurs in front of the vehicle. In this case, the vehicle obtains, based on the first information, that the traffic accident event has been marked at location data of the detected traffic accident event. This improves a trustworthiness degree of information that is about the traffic accident event and that is detected by the front-facing camera of the vehicle.
In this embodiment of this application, performing the map tile update operation based on the first information may be: when it is detected that the first event changes, determining, based on the first information, that the second event changes; determining a target tile to which the second event belongs in the map; and updating the target tile. Specifically, for a map generation side device, new element description information or new event description information may be generated based on newly obtained sensed information and/or calculated information for all map elements or map events within a tile range, to replace corresponding description information stored in a current map. A map update data packet may be further generated based on the new element description information or the new event description information. The map update data packet is marked with a target tile identifier, and version information or update time information that indicates an updated version. The map update data packet is sent to a map using side apparatus, to replace map data that is about the target tile and that is currently stored in the map using side apparatus. The map using side device may receive the map update data packet, and replace some data about the target tile in the map data currently stored in the map using side device with map data about the target tile in the map update data packet.
For example, before performing map releasing, a map server detects, from basic data sent by a data source device (for details, refer to the related description of the data source device in
It can be learned that, in an implementation of this embodiment of this application, map information including association information between events is generated. An event that needs to be referred to in driving decision-making can be predicted or verified based on the map information. This helps improve accuracy of driving decision-making. In addition, a tile including an event associated with a changed event may be preferentially updated or only updated based on an association relationship between the events. This improves map update efficiency.
It should be noted that, both the map generation method and the map using method described in embodiments of this application may be referred to as a map data processing method, and both the map generation apparatus and the map using apparatus described in embodiments of this application may be referred to as a map data processing apparatus.
An embodiment of this application further provides a map system. The map system includes a map generation apparatus and a map using apparatus. The map system is configured to perform the methods described in the foregoing embodiments of this application. The map generation apparatus in this application may be a map server, a component or chip in a map server, a roadside device, a vehicle, a mobile terminal, or a component or chip in a roadside device, a vehicle, or a mobile terminal. The map using apparatus may be a vehicle, a component (for example, a navigation apparatus or an autonomous driving apparatus in a vehicle) in a vehicle, a chip in a vehicle, a portable terminal (for example, a mobile phone, a portable computer, or a navigator), a component in a portable terminal, a chip in a portable terminal, or the like.
Specifically, in the map system, the map generation apparatus is configured to generate, store, and send first information. The first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map. The map using apparatus is configured to: receive the first information, and perform a sensed information fusion operation, an event prediction operation, or a map tile update operation based on the first information.
It should be noted that, the map generation apparatus may be specifically configured to perform the methods described in embodiments of
An embodiment of this application further provides a map. The map includes map information, the map information indicates that there is an association relationship between a first event and a second event, the map information includes association type information, the association type information indicates a type of the association relationship, and the first event and the second event are events in the map.
In a specific implementation, the type of the association relationship includes that the first event is a cause event of the second event, or the first event is a result event of the second event.
In a specific implementation, the map information further includes an association factor, and the association factor indicates a degree of correlation between the first event and the second event.
In a specific implementation, the map information further includes a time correlation factor, and the time correlation factor indicates a degree of time correlation between the first event and the second event.
In a specific implementation, the map information further includes at least one of the following: information indicating overlapping duration between a first time range of the first event and a second time range of the second event; information indicating a first intersection over union between the first time range and the second time range; and information indicating a time interval between the first time range and the second time range. The first intersection over union is a ratio of the overlapping duration to duration of a third time range, and the third time range is a union set of the first time range and the second time range.
In a specific implementation, the map information further includes a spatial correlation factor, and the spatial correlation factor indicates a degree of spatial correlation between the first event and the second event.
In a specific implementation, the map information further includes at least one of the following: information indicating an overlapping area between first location data of the first event and second location data of the second event; information indicating a second intersection over union between the first location data and the second location data; information indicating an interval distance between the first location data and the second location data; and information indicating a degree of traffic reachability between the first location data and the second location data. The second intersection over union is a ratio of the overlapping area to an area of third location data, and the third location data is a union set of the first location data and the second location data.
In a specific implementation, the map information further includes a spatiotemporal correlation factor, and the spatiotemporal correlation factor indicates a degree of time and space correlation between the first event and the second event.
In a specific implementation, the map information further includes at least one of the following: information indicating an identifier of the first event; information indicating an identifier of the second event; information indicating an identifier of a map tile in which the first event is located; and information indicating an identifier of a map tile in which the second event is located.
In a specific implementation, the map information is stored in a data structure that is in the map and that is used to describe the first event or the second event.
In a specific implementation, the map information is stored in a data structure that uses the identifier of the first event and the identifier of the second event as a joint index.
The generation unit 310 is configured to generate first information. The first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map. The storage unit 312 is configured to store the first information as data of the map.
When the method described in the embodiment in
The obtaining unit 410 is configured to obtain first information. The first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map. The processing unit 412 is configured to perform a sensed information fusion operation, an event prediction operation, or a map tile update operation based on the first information.
Functional modules of the map using apparatus 40 may be further configured to implement the map using method described in the embodiment in
Only one or more of the units in embodiments shown in
This application further provides a schematic diagram of a structure of a map generation apparatus. As shown in
The bus 324 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, and the like. Buses may be classified into an address bus, a data bus, a control bus, and the like. For ease of representation, only one line is used in
The processor 321 may include any one or more of a central processing unit (CPU), a microprocessor (MP), or a digital signal processor (DSP).
The memory 323 is configured to provide storage space, and the storage space may store data such as an operating system and a computer program. The memory 323 may be one or a combination of a random access memory (RAM), an erasable programmable read-only memory (EPROM), a read-only memory (ROM), or a portable read-only memory (CD-ROM). The memory 323 may exist independently, or may be integrated into the processor 321.
The communication interface 322 may be configured to provide an information input or output for the processor 321. Alternatively, the communication interface 322 may be configured to receive data sent from the outside and/or send data to the outside, and may be a wired link interface including an Ethernet cable or the like, or may be a wireless link (for example, Wi-Fi, Bluetooth, universal wireless transmission, or an in-vehicle short-range communication technology) interface. Alternatively, the communication interface 322 may further include a transmitter (for example, a radio frequency transmitter or an antenna), a receiver, or the like coupled to the interface.
The processor 321 in the map generation apparatus 32 is configured to read a computer program stored in the memory 323, and is configured to perform the method described in the embodiment in
In a possible design, the map generation apparatus 32 may be one or more modules in the map generation apparatus that performs the method shown in
This application further provides a map using apparatus. As shown in
The bus 424 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, and the like. Buses may be classified into an address bus, a data bus, a control bus, and the like. For ease of representation, only one line is used in
The processor 421 may include any one or more of a central processing unit (CPU), a microprocessor (MP), or a digital signal processor (DSP).
The memory 423 is configured to provide storage space, and the storage space may store data such as an operating system and a computer program. The memory 423 may be one or a combination of a random access memory (RAM), an erasable programmable read-only memory (EPROM), a read-only memory (ROM), or a portable read-only memory (CD-ROM). The memory 423 may exist independently, or may be integrated into the processor 421.
The communication interface 422 may be configured to provide an information input or output for the processor 421. Alternatively, the communication interface 422 may be configured to receive data sent from the outside and/or send data to the outside, and may be a wired link interface including an Ethernet cable or the like, or may be a wireless link (for example, Wi-Fi, Bluetooth, universal wireless transmission, or an in-vehicle short-range communication technology) interface. Alternatively, the communication interface 422 may further include a transmitter (for example, a radio frequency transmitter or an antenna), a receiver, or the like coupled to the interface.
The processor 421 in the map using apparatus 42 is configured to read a computer program stored in the memory 423, and is configured to perform the method described in
In a possible design, the map using apparatus 42 may be one or more modules in the apparatus that performs the method shown in
An embodiment of this application further provides a map data processing apparatus. The map data processing apparatus includes an obtaining unit and a storage unit. The obtaining unit is configured to obtain first information. The first information indicates that there is an association relationship between at least two events, the at least two events include a first event and a second event, the first information includes association type information, the association type information indicates a type of the association relationship, and the at least two events are events in a map. The storage unit is configured to store the first information as data of the map. The obtaining first information may be generating the first information or receiving the first information. In some possible embodiments, the map data processing apparatus further includes a processing unit, configured to perform a sensed information fusion operation, an event prediction operation, or a map tile update operation based on the first information.
The map data processing apparatus may be the map generation apparatus shown in
An embodiment of this application further provides a map data processing apparatus. The map data processing apparatus may be the map generation apparatus shown in
An embodiment of this application further provides a map data processing system. The map data processing system includes a first map data processing apparatus and a second map data processing apparatus. The first map data processing apparatus may be, for example, the map generation apparatus shown in
In the foregoing embodiments in this specification, the description of each embodiment has respective focuses. For a part that is not described in detail in an embodiment, refer to related descriptions in other embodiments.
It should be noted that a person of ordinary skill in the art may see that, all or a part of the steps in each method of the foregoing embodiments may be implemented by a program instructing related hardware. The program may be stored in a computer-readable storage medium. The storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory, (EEPROM), a compact disc read-only memory (CD-ROM) or another optical disk memory, a magnetic disk memory, a magnetic tape memory, or any other computer-readable medium that can be configured to carry or store data.
The technical solutions of this application essentially, or the part contributing to the conventional technology, or all or some of the technical solutions may be implemented in a form of a software product. A computer program product is stored in a storage medium and includes several instructions for instructing a device (which may be a personal computer, a server, a network device, a robot, a single-chip microcomputer, a chip, a robot, or the like) to perform all or some of the steps of the methods described in embodiments of this application.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202110876093.4 | Jul 2021 | CN | national |
This application is a continuation of International Application PCT/CN2022/104197, filed on Jul. 6, 2022, which claims priority to Chinese Patent Application No. 202110876093.4, filed on Jul. 30, 2021. The disclosures of the aforementioned priority applications are hereby incorporated by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/CN2022/104197 | Jul 2022 | US |
| Child | 18423331 | US |
