Map management method and device, equipment and storage medium

Abstract

The invention provides a map management method and device, equipment and a storage medium, and the method comprises the steps: obtaining the traffic information of a target route in a first driving time period when a vehicle passes through the target route in the first driving time period; generating a self-learning map based on the traffic information of the target route in the first driving time period; when the vehicle passes through the target route again in the second driving time period, traffic information of the target route in the second driving time period is acquired; and updating the self-learning map based on the traffic information of the target route in the second driving time period. The method is used for solving the problem that the real-time performance of map information is poor.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a map management method, device, equipment and storage medium. Background Art

[0002] Traditional high-level intelligent driving systems usually require high-precision maps to provide accurate base map information. However, high-precision maps can only collect static road information, and it is difficult to update data in real time and accurately. The production and maintenance costs are high, and the coverage and applicability are low. For the above reasons, high-level driving functions in the field of intelligent driving are undergoing a transition from relying on traditional high-precision maps to adopting map-free solutions. The self-learning map for memory driving is the prototype of the map-free solution, which realizes autonomous cruising on a specific route.

[0003] Memory driving, also known as Commute Navigation on Autopilot (CNOA), is an urban-level assisted driving product. Based on the learned specific route, it completes the automatic assisted driving function from point A to point B, realizing functions such as autonomous steering, lane changing, and obstacle avoidance. Memory driving is mainly divided into three parts: route learning, route cruising, and route management. Memory driving focuses on high-frequency commuting scenarios in cities, does not rely on high-precision maps, relieves users' fatigue driving and stress in commuting scenarios, and provides a more relaxed and enjoyable travel experience.

[0004] Urban roads include not only static elements such as roads, intersections, and lane lines, but also dynamic events such as construction, accidents, and traffic control. The current self-learning maps constructed by memory driving cannot ensure the real-time nature of map information. Summary of the invention

[0005] The present application provides a map management method, apparatus, device and storage medium to solve the problem of poor real-time performance of map information.

[0006] According to a first aspect of an embodiment of the present application, a map management method is provided, including:

[0007] When the vehicle passes through the target route within the first driving time period, obtaining traffic information of the target route within the first driving time period;

[0008] generating a self-learning map based on traffic information of the target route within the first driving time period;

[0009] When the vehicle passes through the target route again within the second driving time period, acquiring traffic information of the target route within the second driving time period;

[0010] The self-learning map is updated based on traffic information of the target route during the second driving time period.

[0011] Optionally, the method further comprises:

[0012] Obtaining self-learning maps corresponding to at least two vehicles;

[0013] Based on the route identification, the self-learning maps corresponding to the at least two vehicles are fused to obtain a fused map.

[0014] Optionally, the self-learning maps corresponding to each of the at least two vehicles include traffic information of at least two routes.

[0015] Optionally, the traffic information includes road level information and lane level information; the road level information includes at least one of road topology, road curvature, road construction and road speed limit; the lane level information includes at least one of lane edge, lane ground marking, lane attributes, lane speed limit and lane construction.

[0016] Optionally, the self-learning map includes the trajectory of the target route, traffic information of the target route and experience data of the target route; the experience data refers to historical driving data of each vehicle when passing through the target route;

[0017] The method further comprises:

[0018] Based on the traffic information of the target route and the empirical data of the target route, the vehicle is controlled to automatically travel along the trajectory of the target route.

[0019] Optionally, the self-learning map further includes evaluation information of the target route;

[0020] Before controlling the vehicle to automatically travel along the trajectory of the target route based on the traffic information of the target route and the empirical data of the target route, the method further includes:

[0021] Based on the evaluation information of the target route, the target route is determined to be a route on which the vehicle can automatically travel.

[0022] Optionally, the evaluation information includes the number of times the user takes over the vehicle and the confidence level;

[0023] The step of determining, based on the evaluation information of the target route, that the target route is a route on which the vehicle can automatically travel comprises:

[0024] When the number of times the user takes over the vehicle on the target route is less than a preset number, and / or the confidence of the target route is greater than a preset confidence, the target route is determined to be a route on which the vehicle can travel automatically.

[0025] According to a second aspect of an embodiment of the present application, a map management device is provided, including:

[0026] A first acquisition unit, configured to acquire traffic information of a target route within a first driving time period when a vehicle passes through the target route within the first driving time period;

[0027] a generating unit, configured to generate a self-learning map based on traffic information of the target route in the first driving time period;

[0028] A second acquisition unit, configured to acquire traffic information of the target route within the second driving time period when the vehicle passes through the target route again within the second driving time period;

[0029] An updating unit is used to update the self-learning map based on the traffic information of the target route in the second driving time period.

[0030] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including a memory and a processor;

[0031] The memory is connected to the processor and is used to store programs;

[0032] The processor is used to implement the map management method as described in the first aspect by running the program in the memory.

[0033] According to a fourth aspect of an embodiment of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the map management method as described in the first aspect is implemented.

[0034] In the present application, when the vehicle passes through the target route for the first time, the traffic information of the target route in the first driving time period is obtained, and then a self-learning map is generated. When the vehicle passes through the target route again, the traffic information of the target route in the second driving time period is obtained, and then the self-learning map is updated based on the traffic information of the target route in the second driving time period. By having the vehicle pass the same target route multiple times in different driving time periods, the traffic information of the target route in multiple driving time periods is obtained, and then the self-learning map is updated, thereby realizing dynamic updating of the traffic information of the target route and improving the real-time nature of the map information in the self-learning map. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0036] Figure 1 A flowchart of a map management method provided in an embodiment of the present application;

[0037] Figure 2 A flowchart of a map management method provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of a map management device provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a map management system provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] Exemplary Implementation Environment

[0043] According to the map management method of the embodiment of the present application, it can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a cloud server capable of cloud computing. The method can be implemented by a processor calling a computer-readable program instruction stored in a memory. The present application takes the map management method executed by a server as an example for explanation, but does not limit this.

[0044] Exemplary Methods

[0045] See also Figure 1 In an exemplary embodiment, a map management method is provided. Figure 1 As shown, the process of the map management method mainly includes:

[0046] Step 101: When a vehicle passes through a target route within a first driving time period, traffic information of the target route within the first driving time period is obtained.

[0047] In some embodiments, traffic information includes road level information and lane level information; road level information includes at least one of road topology, road curvature, road construction and road speed limit; lane level information includes at least one of lane edge, lane ground markings, lane attributes, lane speed limit and lane construction.

[0048] In an exemplary embodiment, as shown in Table 1, it is a content description table of traffic information.

[0049] Table 1

[0050]

[0051] In Table 1, traffic information includes road-level information and lane-level information; road-level information includes road topology, road curvature, road construction and road speed limit; road topology includes attachment relationship and road connection relationship; road curvature refers to road-level curvature; road construction refers to road-level construction information; road speed limit refers to road-level speed limit information; lane-level information includes lane edge lines, lane ground markings, lane attributes, lane speed limits and lane construction; lane edge lines include lane color, type, curvature, lane geometry changes (increase and decrease lines, positions); lane ground markings include lane lines, lane arrows, etc.; lane attributes include lane attributes such as restricted traffic and time limits; lane speed limits refer to lane-level speed limit information; lane construction refers to lane-level construction information.

[0052] In some embodiments, step 101 may be that when a vehicle passes through a target route in a first driving time period, the vehicle collects traffic information of the target route in the first driving time period and sends it to the vehicle's onboard device, and the cloud server obtains the traffic information of the target route in the first driving time period uploaded by the vehicle's onboard device. At the same time, the vehicle's onboard device stores the traffic information of the target route in the first driving time period locally. The cloud server stores the traffic information of the target route in the first driving time period.

[0053] Step 102: Generate a self-learning map based on traffic information of the target route in a first driving time period.

[0054] In some embodiments, the self-learning map includes a trajectory of the target route and traffic information of the target route.

[0055] Step 103: When the vehicle passes the target route again in the second driving time period, traffic information of the target route in the second driving time period is obtained.

[0056] In an exemplary embodiment, when the vehicle passes the target route again during the second driving time period, the vehicle automatically collects the traffic information of the target route during the second driving time period and sends it to the vehicle's onboard device. The cloud server obtains the traffic information of the target route during the second driving time period automatically uploaded by the vehicle's onboard device. When the vehicle passes the previously memorized and learned target route again, the traffic information of the target route during the second driving time period will be automatically uploaded, and the cloud server will be automatically triggered to update the self-learning map based on the traffic information of the target route during the second driving time period, without the need for manual operation by the user. At the same time, the vehicle's onboard device stores the traffic information of the target route during the second driving time period locally. The cloud server stores the traffic information of the target route during the second driving time period.

[0057] Step 104: Update the self-learning map based on the traffic information of the target route in the second driving time period.

[0058] In some embodiments, step 104 may be to eliminate duplicate information (i.e., eliminate old elements) from the traffic information of the target route in the second driving time period and the traffic information of the target route in the first driving time period, and update the differential elements of the traffic information of the target route in the second driving time period relative to the traffic information of the target route in the first driving time period.

[0059] The target route that the vehicle passes through may have map timeliness issues caused by changes in the environment and road information. The cloud server integrates the traffic information of the target route in multiple driving time periods based on the traffic information of the target route in multiple driving time periods uploaded by the vehicle's on-board equipment. The vehicle passes the target route multiple times, and the overlapping information in the traffic information of the target route in multiple driving time periods is integrated, redundant data is eliminated, and differential elements are updated to achieve updated self-learning maps, thereby generating a complete union map and improving the effectiveness of the map.

[0060] In some embodiments, Figure 2 As shown, the map management method also includes:

[0061] Step 201, obtaining self-learning maps corresponding to at least two vehicles.

[0062] In some embodiments, the self-learning maps corresponding to at least two vehicles each include traffic information of a route, so as to realize single-vehicle multi-trip fusion and multi-vehicle multi-trip fusion of the self-learning maps corresponding to the same route.

[0063] In some embodiments, the self-learning maps corresponding to at least two vehicles each include traffic information of at least two routes. The self-learning maps corresponding to multiple routes can be fused for multiple trips of a single vehicle and multiple trips of multiple vehicles, and the maps of different vehicles of the same model, multiple models of vehicles, and multiple regions can be fused, so as to realize a crowdsourced map used by multiple vehicles on one map.

[0064] Step 202: Based on the route identification, the self-learning maps corresponding to at least two vehicles are fused to obtain a fused map.

[0065] In an exemplary embodiment, each learned route will have a corresponding unique route identifier, which can be represented by a link. Based on the route identifier, the self-learning maps corresponding to at least two vehicles are fused, and the maps are fused by road-level association, fusion of multi-map tracks, splicing of map edges, etc. to obtain a fused map.

[0066] Obtain the self-learning maps corresponding to at least two vehicles, and fuse the self-learning maps corresponding to at least two vehicles based on route identification to obtain a fused map, thereby realizing the fusion of maps of different vehicles of the same model, vehicles of multiple models, and multiple regions, and realizing a crowdsourced map that can be used by multiple vehicles on one map.

[0067] In some embodiments, the self-learning map includes a trajectory of the target route, traffic information of the target route, and experience data of the target route; the experience data refers to historical driving data of each vehicle when passing through the target route.

[0068] In some embodiments, the experience data includes lane experience data and intersection experience data; the lane experience data includes at least one of experience vehicle speed, experience lane construction, experience lane change interval and experience lane change trajectory; the intersection experience data includes at least one of intersection experience trajectory, experience stopping point before intersection and experience traffic light.

[0069] In the exemplary embodiment, as shown in Table 2, it is a content description table of the experience data.

[0070] Table 2

[0071]

[0072] In Table 2, PNC (Proportional-Nonlinear Control) is a controller. In Table 2, the experience data includes lane experience data and intersection experience data. Lane experience data includes experience speed, experience lane construction, experience lane change interval and experience lane change trajectory.

[0073] The empirical speed can be used in scenarios such as setting a target speed that conforms to human driving habits, optimizing speed limit strategies where speed limits are unreasonable, and adjusting the speed in schools, crosswalks, intersections, etc.; it is necessary to record the temporal and spatial characteristics of the empirical speed (i.e., when and where the vehicle travels at the empirical speed). The empirical speed here refers to the lane-level empirical speed, and the mean and variance of the empirical speed can be calculated as a reference for the user's speed setting or adjustment.

[0074] The application scenarios of experience lane construction include notifying users in advance of taking over in long-term construction sections, and using lane-level construction information as PNC decision input to plan lane changes. Experience lane construction can be judged, and if there is no construction within 100 meters before and after, it is considered that the experience lane construction is lifted, and the experience lane construction can be updated in real time. When there is a certain distance between the vehicle and the construction lane, the vehicle can be notified of experience lane construction in advance. The experience lane construction here refers to lane-level construction information.

[0075] The application scenarios of the empirical lane change interval and empirical lane change trajectory include the planned lane change interval that conforms to the user's experience, and the user's empirical trajectory reference in the divergence and merging scenarios. The empirical lane change interval and empirical lane change trajectory are used for lane-level lane change interval recommendation. The lane change interval needs to be associated with the import and export information, and the lane change interval needs to be combined with the road condition information.

[0076] The intersection experience data includes the intersection experience trajectory, the experience stop point before the intersection and the experience traffic lights.

[0077] The application scenarios of intersection experience trajectory include turning at intersections based on user experience paths and providing PNC with reference paths. The recommended intersection experience trajectory integrates the experience of multiple users, and the intersection trajectory is smooth and complies with basic traffic regulations.

[0078] The application scenarios of the experience stop point before the intersection include deceleration based on the experience stop position in the blind spot of the road gap, and stopping based on the user's experience stop point in the absence of a stop line. If the speed of the vehicle is close to 0 and there is no vehicle ahead in the observation data for multiple times, it is the experience stop point before the intersection. The experience stop point before the intersection is in the longitudinal position of the lane.

[0079] The application scenarios of experience traffic lights include when traffic lights fail, user experience data can be used as a reference for decision making (such as sensing an abnormal traffic light and multiple users choose to go straight through at the same time). Experience traffic lights can be used in the lanes and directions corresponding to traffic lights.

[0080] In some embodiments, the map management method further includes: controlling the vehicle to automatically travel along the trajectory of the target route based on traffic information of the target route and empirical data of the target route.

[0081] Empirical data refers to the historical driving data of each vehicle when passing the target route. On the basis of the base map, the historical driving data of multiple vehicles when passing the target route are combined to enrich the map information and better meet the user's driving needs. Based on the traffic information of the target route and the empirical data of the target route, the vehicle is controlled to automatically drive along the trajectory of the target route, further enhancing the real-time nature of the map, more in line with the user's driving habits, and further meeting the latest driving needs of users' commonly used commuting routes.

[0082] In some embodiments, the self-learning map also includes evaluation information of the target route.

[0083] In some embodiments, the evaluation information may be a part of ODD (Operational Design Domain) information. The evaluation information includes takeover hotspot information and confidence, and the ODD information includes evaluation information and operational information.

[0084] In an exemplary embodiment, as shown in Table 3, it is a content description table of ODD information.

[0085] Table 3

[0086]

[0087] In Table 3, ODD information includes evaluation information and operation information. Evaluation information includes takeover hotspot information and confidence.

[0088] The application scenarios of takeover hotspot information include: 1. When using any assisted driving function, the frequency of user takeover / intervention of vehicle speed and steering wheel in different sections is presented through heat maps; 2. High-frequency takeover is positively correlated with scene complexity (such as large curvature curves, users frequently intervene in the throttle when braking at abnormal intersections, large vehicles approaching scenes, and users frequently intervene in the steering wheel in narrow lanes), and intervention is positively correlated with scene difficulty; 3. For routes that users have learned and completed on relevant sections, users can be prompted to pay close attention or temporarily close relevant routes. Takeover heat maps can be presented on the map, including data such as the number of takeovers on the same section; the reasons for user takeover can support drill-down analysis; customized policy tasks can be issued for different sections, such as suppression of opening, safety warning, function degradation, etc.; lane-level information should be supported; and the average and maximum values ​​of takeover should be provided. Among them, takeover refers to the user participating in driving the vehicle, not pure autonomous driving. The average and maximum values ​​of takeover refer to the average and maximum values ​​of the number of takeovers by different users on the same section, which are the takeover data on a certain road for operators to judge the availability of the section. The reason for user takeover will not be displayed to the user and is only used for collection and analysis by the operation system.

[0089] The application scenarios of confidence include: after learning is completed, the confidence of the available route is calculated, and the available routes are released in sections. Lane-level and road-level information should be supported, and route confidence should be calculated in sections.

[0090] Application scenarios of operation information include: 1. Editing, opening, and closing relevant road sections; 2. Uploading and sending operation self-learning maps to user vehicles (such as popular commuting routes); 3. Supporting analysis of road section-related user events (such as taking over hot spots, etc.). Relevant visualization and operation tools can be provided.

[0091] ODD information supports the demarcation of available fence ranges for routes, and the available road sections at the dispatch end are determined based on operational management, hot events taken over by multiple users, and the confidence level of algorithm judgment.

[0092] In some embodiments, before controlling the vehicle to automatically drive along the trajectory of the target route based on the traffic information of the target route and the empirical data of the target route, the map management method also includes: determining that the target route is a route on which the vehicle can automatically drive based on the evaluation information of the target route.

[0093] Before controlling the vehicle to automatically drive along the trajectory of the target route based on the traffic information of the target route and the experience data of the target route, the target route is determined to be a route on which the vehicle can drive automatically based on the evaluation information of the target route. When it is determined that the target route can be used for automatic driving based on the evaluation information of the target route, the target route can be sent to the vehicle's on-board equipment, and the vehicle can be controlled to automatically drive along the trajectory of the target route based on the traffic information of the target route and the experience data of the target route, thereby avoiding frequent takeovers and the like, and improving the user's driving experience.

[0094] In some embodiments, the evaluation information includes the number of times the user takes over the vehicle and the confidence level.

[0095] In some embodiments, based on the evaluation information of the target route, determining the target route as a route on which the vehicle can travel automatically includes: determining the target route as a route on which the vehicle can travel automatically when the number of times the user takes over the vehicle on the target route is less than a preset number, and / or the confidence level of the target route is greater than a preset confidence level.

[0096] In an exemplary embodiment, the number of times that a user takes over a vehicle on a target route may be an average of the number of times that a plurality of users take over a vehicle on the target route.

[0097] When the number of times the user takes over the vehicle on the target route is less than the preset number, and / or the confidence of the target route is greater than the preset confidence, the target route is determined to be a route on which the vehicle can drive automatically. When the number of times the target route is taken over is small, and / or the confidence is high, the target route is determined to be a route on which the vehicle can drive automatically. This improves the accuracy of the route on which the vehicle can drive automatically, avoids frequent takeovers, and improves the user's driving experience.

[0098] In some embodiments, the cloud server can also perform map management, support map management by user account, vehicle VIN (Vehicle Identification Number), support map management by region, by link (route identification), and support response to ODD operations on routes.

[0099] Realize data visualization in the cloud, analyze user usage data, analyze functional performance data, close the urban scene data loop, upload maps, and manage maps to ensure that basic data is complete and accurate, and remember that basic driving data can be analyzed and evolved, providing a solid foundation for map updates and optimizations.

[0100] In summary, in the present application, the vehicle passes through the target route for the first time, and the traffic information of the target route in the first driving time period is obtained, and then a self-learning map is generated; when the vehicle passes through the target route again, the traffic information of the target route in the second driving time period is obtained, and then based on the traffic information of the target route in the second driving time period, the self-learning map is updated; by having the vehicle pass the same target route multiple times in different driving time periods, the traffic information of the target route in multiple driving time periods is obtained, and then the self-learning map is updated, thereby realizing dynamic updating of the traffic information of the target route and improving the real-time nature of the map information in the self-learning map.

[0101] Exemplary Devices

[0102] Accordingly, the present application also provides a map management device, such as Figure 3 As shown, the map management device includes:

[0103] A first acquisition unit 301 is used to acquire traffic information of a target route in a first driving time period when a vehicle passes through the target route in the first driving time period;

[0104] A generating unit 302, configured to generate a self-learning map based on traffic information of the target route in the first driving time period;

[0105] A second acquisition unit 303 is used to acquire traffic information of the target route in the second driving time period when the vehicle passes through the target route again in the second driving time period;

[0106] The updating unit 304 is used to update the self-learning map based on the traffic information of the target route in the second driving time period.

[0107] The map management device provided in this embodiment belongs to the same application concept as the map management method provided in the above embodiments of this application, can execute the map management method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the map management method. For technical details not fully described in this embodiment, please refer to the specific processing content of the map management method provided in the above embodiments of this application, and will not be repeated here.

[0108] The functions implemented by the above-mentioned first acquisition unit 301, generation unit 302, second acquisition unit 303 and update unit 304 may be implemented by the same or different processors respectively, which is not limited in the embodiment of the present application.

[0109] It should be understood that the units in the above devices can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device, wherein the processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory in the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits, and the functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are realized by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit can be implemented by PLD, taking FPGA as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by the configuration file, so as to realize the functions of some or all of the above units. All units of the above devices can be implemented in the form of a processor calling software, or in the form of hardware circuits, or in part by a processor calling software, and the remaining part is implemented in the form of hardware circuits.

[0110] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and run instructions, such as a CPU, a microprocessor, a GPU, or a DSP; in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0111] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0112] In addition, all or part of the units in the above device can be integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a SOC. The SOC may include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The type of the at least one processor may be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0113] Exemplary Systems

[0114] Optionally, the present application embodiment also provides a map management system, such as Figure 4 As shown, the map management system includes a vehicle-side map module and a cloud-side map module.

[0115] The vehicle-side map module includes a single-pass mapping submodule, a multi-pass mapping and matching submodule, a data storage submodule and a map management submodule.

[0116] The cloud map module includes a map storage submodule, a map update submodule, a map fusion submodule, an experience layer, an ODD layer and a map management submodule.

[0117] The map update submodule includes road level information and lane level information, and performs old element removal and differential element update.

[0118] The map fusion submodule is used for road association, trajectory fusion and map stitching.

[0119] The experience layer includes the lane experience layer and the intersection experience layer.

[0120] ODD layers include route operation layer, takeover hotspot layer and map confidence layer.

[0121] The vehicle-side map module uploads single-trip map data and multi-trip map data to the cloud-side map module, and the cloud-side map module sends the updated differential map, fusion map, experience data, and ODD fence to the vehicle-side map module, providing dynamic events while ensuring the freshness of static elements of the map.

[0122] Exemplary Electronic Devices

[0123] An embodiment of the present application provides an electronic device, see Figure 5 As shown, the device includes:

[0124] Memory 200 and processor 210;

[0125] The memory 200 is connected to the processor 210 and is used to store programs;

[0126] The processor 210 is used to implement the map management method disclosed in any of the above embodiments by running the program stored in the memory 200.

[0127] Specifically, the electronic device may further include: a bus, a communication interface 220 , an input device 230 and an output device 240 .

[0128] The processor 210, the memory 200, the communication interface 220, the input device 230 and the output device 240 are connected to each other via a bus.

[0129] A bus may include a pathway that transfers information between components of a computer system.

[0130] The processor 210 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0131] The processor 210 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0132] The memory 200 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0133] The input device 230 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0134] Output device 240 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0135] The communication interface 220 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0136] The processor 210 executes the program stored in the memory 200 and calls other devices, which can be used to implement each step of any map management method provided in the above embodiments of the present application.

[0137] Exemplary computer program products and storage media

[0138] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the map management method according to various embodiments of the present application described in any of the above embodiments of this specification.

[0139] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0140] In addition, the embodiment of the present application may also be a storage medium on which a computer program is stored. The computer program is executed by a processor to execute the steps of the map management method according to various embodiments of the present application described in any of the above embodiments of this specification, and specifically the following steps may be implemented:

[0141] Step 101, when a vehicle passes through a target route within a first driving time period, traffic information of the target route within the first driving time period is obtained;

[0142] Step 102, generating a self-learning map based on traffic information of the target route in the first driving time period;

[0143] Step 103, when the vehicle passes the target route again in the second driving time period, obtaining traffic information of the target route in the second driving time period;

[0144] Step 104: Update the self-learning map based on the traffic information of the target route in the second driving time period.

[0145] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0146] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0147] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0148] The modules and sub-modules in the devices and terminals in the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0149] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0150] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0152] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0153] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0154] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0155] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A map management method, characterized in that: include: When the vehicle passes through the target route within the first driving time period, obtaining traffic information of the target route within the first driving time period; generating a self-learning map based on traffic information of the target route within the first driving time period; When the vehicle passes through the target route again within the second driving time period, acquiring traffic information of the target route within the second driving time period; The self-learning map is updated based on traffic information of the target route during the second driving time period.

2. The map management method according to claim 1, characterized in that: The method further comprises: Obtaining self-learning maps corresponding to at least two vehicles; Based on the route identification, the self-learning maps corresponding to the at least two vehicles are fused to obtain a fused map.

3. The map management method according to claim 2, characterized in that: The self-learning maps corresponding to the at least two vehicles respectively include traffic information of at least two routes.

4. The map management method according to claim 1, characterized in that: The traffic information includes road level information and lane level information; the road level information includes at least one of road topology, road curvature, road construction and road speed limit; the lane level information includes at least one of lane edge, lane ground marking, lane attribute, lane speed limit and lane construction.

5. The map management method according to claim 1, characterized in that: The self-learning map includes a trajectory of the target route, traffic information of the target route, and experience data of the target route; The empirical data refers to the historical driving data of each vehicle when passing through the target route; The method further comprises: Based on the traffic information of the target route and the empirical data of the target route, the vehicle is controlled to automatically travel along the trajectory of the target route.

6. The map management method according to claim 5, characterized in that: The self-learning map also includes evaluation information of the target route; Before controlling the vehicle to automatically travel along the trajectory of the target route based on the traffic information of the target route and the empirical data of the target route, the method further includes: Based on the evaluation information of the target route, the target route is determined to be a route on which the vehicle can automatically travel.

7. The map management method according to claim 6, characterized in that: The evaluation information includes the number of times the user takes over the vehicle and the confidence level; The step of determining, based on the evaluation information of the target route, that the target route is a route on which the vehicle can automatically travel comprises: When the number of times the user takes over the vehicle on the target route is less than a preset number, and / or the confidence of the target route is greater than a preset confidence, the target route is determined to be a route on which the vehicle can travel automatically.

8. A map management device, characterized in that: include: A first acquisition unit, configured to acquire traffic information of a target route within a first driving time period when a vehicle passes through the target route within the first driving time period; a generating unit, configured to generate a self-learning map based on traffic information of the target route in the first driving time period; A second acquisition unit, configured to acquire traffic information of the target route within the second driving time period when the vehicle passes through the target route again within the second driving time period; An updating unit is used to update the self-learning map based on the traffic information of the target route in the second driving time period.

9. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the map management method as described in any one of claims 1 to 7 by running the program in the memory.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the map management method according to any one of claims 1 to 7 is implemented.

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