Vehicle control method and device, medium and vehicle

Abstract

The invention relates to a vehicle control method and device, a medium and a vehicle, and relates to the technical field of vehicles, the control method comprises the steps that navigation information of the vehicle is acquired, and the navigation information comprises a target interest point corresponding to a terminal parking lot in a navigation map; determining a target high-precision parking lot map according to the target interest point and an association relationship between different interest points in the navigation map and different high-precision parking lot maps; and when the vehicle runs near the target interest point, controlling the vehicle to run through the target high-precision parking lot map. Through the control method disclosed by the invention, when the vehicle runs to the vicinity of the target interest point, the target high-precision parking lot map can be quickly called according to the association relationship, the conventional road running of the navigation map and the quick and smooth switching of the parking scene of the parking lot can be realized, and the driving experience feeling of a user is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular to a vehicle control method, device, medium, and vehicle. Background Art

[0002] In related technologies, the high-precision map of some parking lots is a thematic map dedicated to automatic parking. Due to its particularity and complexity, the internal roads of the parking lot will not be expressed in the navigation map, and the navigation will end near the entrance of the parking lot. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a vehicle control method, device, medium and vehicle.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a vehicle control method, comprising:

[0005] Obtaining navigation information for the vehicle, the navigation information including a target point of interest corresponding to a destination parking lot in a navigation map;

[0006] Determining a target high-precision parking lot map based on the target point of interest and associations between different points of interest in the navigation map and different high-precision parking lot maps;

[0007] When the vehicle travels near the target point of interest, the vehicle is controlled to travel according to the target high-precision parking lot map.

[0008] Optionally, the association relationship is obtained in the following manner:

[0009] Obtain the positioning information of the parking lot to be calibrated;

[0010] Determining, based on the positioning information, candidate points of interest near the parking lot to be calibrated in the navigation map and a high-precision parking lot map to be associated with the parking lot to be calibrated;

[0011] Establish an association relationship between the candidate points of interest and the high-precision parking lot map to be associated.

[0012] Optionally, establishing an association relationship between the candidate point of interest and the high-precision parking lot map to be associated includes:

[0013] Determine the entrance and exit locations of the parking lot to be calibrated based on the high-precision parking lot map to be associated;

[0014] Determining a point of interest to be associated based on the elements of the candidate point of interest;

[0015] The association relationship is established according to the distance between the entrance and exit location point and the point of interest to be associated.

[0016] Optionally, determining the point of interest to be associated based on the elements of the candidate point of interest includes:

[0017] When the element condition indicates that the candidate points of interest do not include a point of interest element, generating a virtual point of interest in the high-precision parking lot map to be associated, wherein the location of the virtual point of interest is near the entrance and exit of the parking lot to be calibrated;

[0018] The virtual point of interest is associated with the candidate point of interest, and the virtual point of interest is determined as the point of interest to be associated.

[0019] Optionally, obtaining the point of interest to be associated according to the element conditions of the candidate point of interest includes:

[0020] When the element condition indicates that the candidate interest points include interest point elements, the candidate interest points are determined as the interest points to be associated.

[0021] Optionally, establishing the association relationship according to the distance between the entrance and exit location point and the point of interest to be associated includes:

[0022] When the distance is less than a preset distance threshold, associating the entrance and exit location point with the point of interest to be associated;

[0023] When the distance is greater than or equal to the preset distance threshold, a connecting road is generated on the high-precision parking lot map to be associated, and the connecting road is associated with the point of interest to be associated, wherein the connecting road is used to connect the entrance and exit location points and the point of interest to be associated.

[0024] Optionally, the navigation information further includes a navigation path. Before controlling the vehicle to travel using the target high-precision parking lot map, the method further includes:

[0025] Determining, based on the navigation path, a high-precision road map matching the navigation path;

[0026] According to the high-precision road map, the vehicle is controlled to travel toward the target point of interest.

[0027] According to a second aspect of an embodiment of the present disclosure, there is provided a vehicle control device, comprising:

[0028] an acquisition module configured to acquire navigation information of the vehicle, the navigation information including a target point of interest corresponding to a destination parking lot in a navigation map;

[0029] a determination module configured to determine a target high-precision parking lot map based on the target point of interest and associations between different points of interest and different high-precision parking lot maps in the navigation map;

[0030] The control module is configured to control the vehicle to travel by using the target high-precision parking lot map when the vehicle travels near the target point of interest.

[0031] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the vehicle control method provided by the first aspect of the embodiment of the present disclosure is implemented.

[0032] According to a fourth aspect of an embodiment of the present disclosure, a vehicle is provided, comprising:

[0033] a storage device for storing a computer program;

[0034] An execution device is used to execute the computer program to implement the vehicle control method provided by the first aspect of the embodiment of the present disclosure.

[0035] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0036] The present disclosure determines a target high-precision parking lot map based on the target point of interest (POI) corresponding to the destination parking lot in the navigation map, as well as the associations between different POIs and different high-precision parking lot maps in the navigation map, included in the vehicle's navigation information. When the vehicle approaches the target POI, the target high-precision parking lot map is used to control vehicle movement. This allows the vehicle to quickly access the target high-precision parking lot map based on the associations when it approaches the target POI, enabling fast and smooth switching between regular road driving and parking scenarios on the navigation map, enhancing the user's driving experience.

[0037] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0039] Figure 1 The figure is a flow chart showing a vehicle control method according to an exemplary embodiment.

[0040] Figure 2 is a schematic diagram of a scenario in which no point of interest elements are included in candidate points of interest according to an exemplary embodiment.

[0041] Figure 3 The diagram is a schematic diagram of a scene in which candidate points of interest include point of interest elements according to an exemplary embodiment.

[0042] Figure 4 The figure is a flowchart of a vehicle navigation method according to an exemplary embodiment.

[0043] Figure 5 is a block diagram of a vehicle control device according to an exemplary embodiment.

[0044] Figure 6 is a block diagram of a vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0045] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0046] A high-definition map (HD map) is a thematic map currently used primarily for autonomous driving. It can produce a variety of high-precision road network attributes and feature information to meet the needs of autonomous driving. A high-precision map is a vector map that primarily expresses road traffic data models and traffic rules through three-dimensional curves such as roads, lanes, and lane markings. A navigation map is a thematic map used in satellite navigation software and is currently widely used for car navigation and assisted driving. It is primarily used for route planning and navigation functions, such as point of interest (POI) search, route planning, voice navigation, and map rendering.

[0047] Current intelligent connected vehicles typically use both high-precision maps and navigation maps to implement autonomous driving and navigation functions, respectively. Typically, the autonomous driving system and the in-vehicle navigation system operate independently on separate hardware and software. To provide a better service experience during autonomous driving, traditional navigation map services can continue to be used during autonomous driving, enabling smooth transitions between human and intelligent driving without requiring users to navigate multiple times. This requires system-level collaboration between high-precision maps and navigation map applications to achieve linkage between the intelligent driving system and the navigation system.

[0048] For example, while a vehicle is in motion, the navigation system receives satellite positioning coordinates, performs navigation map matching to implement navigation, and shares information related to the navigation path with the autonomous driving system. The autonomous driving system receives the satellite coordinates and path information from the navigation system, performs high-precision map matching, and thus implements autonomous driving along the navigation path. This linkage function only works on roads covered by both high-precision maps and navigation maps, such as highways and urban road networks. However, in underground parking scenarios, for example, the navigation map only provides basic location and attribute information of points of interest (POIs) at the entrance and exit of the parking lot, while the high-precision map provides high-precision elements such as the detailed road network and parking spaces within the garage. Therefore, the linkage function between the intelligent driving system and the navigation system cannot be implemented in underground parking scenarios. Navigation will end before the user enters the parking lot entrance. As a result, the autonomous driving system cannot obtain the path information and entrance and exit information from the navigation system, and therefore cannot quickly load the corresponding parking lot's high-precision map. Further, it is impossible to quickly and smoothly switch to autonomous driving or AVP (Automated Valet Parking) functions in the underground parking lot.

[0049] See also Figure 1 , Figure 1 is a flow chart of a vehicle control method according to an exemplary embodiment. Figure 1 As shown, the vehicle control method is used in a vehicle and includes the following steps.

[0050] In step S101, navigation information of the vehicle is obtained, where the navigation information includes a target point of interest corresponding to a destination parking lot in a navigation map.

[0051] In step S102, a target high-precision parking lot map is determined based on the target point of interest and the association between different points of interest and different high-precision parking lot maps in the navigation map.

[0052] In step S103, when the vehicle travels to the vicinity of the target point of interest, the vehicle is controlled to travel using the target high-precision parking lot map.

[0053] For example, the vehicle control method described herein can be used in scenarios where the vehicle navigation destination is a parking lot, and furthermore, in scenarios where the navigation destination is an underground parking lot. The vehicle control method can also be extended to scenarios where the navigation map contains location information for the target location, but does not contain a detailed path within the target location, while a high-precision map database contains high-precision map data for the target location.

[0054] For example, a point of interest is a term used to represent a geographically located point. In a geographic information system, a point of interest is the coordinates of a specific location, such as a store, restaurant, park, hospital, bank, or any other entity with a geographical location. Points of interest are often used in applications such as navigation, maps, and location services to help users find the places they want to go. For example Figure 2 and Figure 3 As shown, "XX Airport", "Terminal 3", "South Parking Lot" and "Underground Parking Lot (Entrance)" etc. marked in the figure can be considered as the names of the points of interest marked on the navigation map.

[0055] For example, navigation maps have low accuracy, typically around 10 meters, while high-precision maps can achieve centimeter-level accuracy. High-precision parking lot maps can be found by searching the HD map database. This association is established by pre-establishing an association between points of interest in the navigation map and the corresponding HD parking lot maps.

[0056] For example, a navigation system receives the vehicle's current location transmitted from a satellite and the navigation destination specified by the user on the navigation system, and performs route planning to obtain navigation information. This navigation information includes the navigation route from the vehicle's current location to the navigation destination, points of interest at the navigation destination, and road traffic information along the navigation route. The intelligent driving system receives the vehicle's current location coordinates transmitted from the satellite and the navigation information from the navigation system. Upon obtaining the target point of interest at the destination parking lot in the navigation information, the system matches the target high-precision parking lot map corresponding to the target point of interest based on the target point of interest and its association with the high-precision parking lot map. This allows the vehicle to pre-load the high-precision parking lot map corresponding to the destination parking lot during driving. When the vehicle reaches the target point of interest, it can smoothly switch to a scenario where the high-precision parking lot map is used to control vehicle driving. The vehicle can then perform route planning, automatic parking, autonomous driving, and so on. Autonomous driving can be fully autonomous or semi-autonomous. Fully autonomous driving refers to an intelligent driving method that uses the vehicle's onboard sensor system to perceive the road environment, automatically plan the driving route, and control the vehicle to the predetermined destination, without driver control. Semi-automatic driving refers to a system in which the vehicle has a system that integrates lateral and longitudinal control functions under limited road and environmental conditions. The driver does not need to control these functions, but the driver needs to intervene manually in an emergency.

[0057] Here, the matching process between the target POI and the target high-precision parking map, as well as the loading process of the target high-precision parking map, can begin or end when the vehicle approaches the target POI, and this is not limited here. Therefore, step S102 can be understood as the process of determining a target high-precision parking map that matches the target POI from the association relationship, or the process of determining whether a target high-precision parking map corresponding to the target POI exists in the association relationship.

[0058] The present disclosure determines a target high-precision parking lot map based on the target point of interest (POI) corresponding to the destination parking lot in the navigation map, as well as the associations between different POIs and different high-precision parking lot maps in the navigation map, included in the vehicle's navigation information. When the vehicle approaches the target POI, the target high-precision parking lot map is used to control vehicle movement. This allows the vehicle to quickly access the target high-precision parking lot map based on the associations when it approaches the target POI, enabling fast and smooth switching between regular road driving and parking scenarios on the navigation map, enhancing the user's driving experience.

[0059] Furthermore, by matching points of interest with high-precision maps through preset relationships, there is no need to perform real-time matching and calibration of navigation maps and high-precision maps during vehicle driving. This saves some computing power while ensuring the accuracy of the autonomous driving system in matching the parking lot entrance road and parking lot entrance.

[0060] As an optional embodiment, the association relationship is obtained in the following manner:

[0061] Obtain the positioning information of the parking lot to be calibrated;

[0062] Determining, based on the positioning information, candidate points of interest near the parking lot to be calibrated in the navigation map and a high-precision parking lot map to be associated with the parking lot to be calibrated;

[0063] Establish an association relationship between the candidate points of interest and the high-precision parking lot map to be associated.

[0064] For example, when establishing the association, the navigation system and the autonomous driving system can each obtain the positioning information of the parking lot to be calibrated to determine its basic location and attribute information. The navigation system can then identify points of interest (POIs) for the parking lot on the navigation map based on the positioning information. The autonomous driving system can then search the HD map database for HD map data for the parking lot based on the positioning information. If HD map data exists for the parking lot, the autonomous driving system can then retrieve the HD map data for the parking lot and associate it with the POIs for the parking lot on the navigation map.

[0065] As an optional embodiment, establishing an association relationship between the candidate point of interest and the high-precision parking lot map to be associated includes:

[0066] Determine the entrance and exit locations of the parking lot to be calibrated based on the high-precision parking lot map to be associated;

[0067] Determining a point of interest to be associated based on the elements of the candidate point of interest;

[0068] The association relationship is established according to the distance between the entrance and exit location point and the point of interest to be associated.

[0069] For example, for some underground garage scenarios, navigation maps only provide basic location and attribute information for points of interest (POIs) at the garage's entrances and exits. High-precision parking lot maps, on the other hand, provide high-precision elements such as the detailed road network and parking spaces within the parking lot. POI elements can include name, address, coordinates, category, and so on. POIs to be associated can be determined based on whether the POI element is included in the POI. Furthermore, an association relationship is established between the candidate POIs and the high-precision parking lot map to be associated based on the distance between the entrance and exit locations and the POI to be associated.

[0070] As an optional embodiment, determining the point of interest to be associated according to the elements of the candidate point of interest includes:

[0071] When the element condition indicates that the candidate points of interest do not include a point of interest element, generating a virtual point of interest in the high-precision parking lot map to be associated, wherein the location of the virtual point of interest is near the entrance and exit of the parking lot to be calibrated;

[0072] The virtual point of interest is associated with the candidate point of interest, and the virtual point of interest is determined as the point of interest to be associated.

[0073] For example, when the candidate points of interest do not include point of interest elements, virtual points of interest can be generated in the high-precision parking map to be associated with the candidate points of interest, and the virtual points of interest associated with the candidate points of interest are used as points of interest to be associated with the high-precision parking map. Figure 2For example, when searching for "XX Airport Parking Lot" as the navigation destination on the navigation map, since the airport parking lot does not include a specific POI, the resulting candidate POI may be "XX Airport." In this case, a virtual POI can be generated near the parking lot entrance or exit in the high-precision parking lot map and associated with the "XX Airport" POI in the navigation map. This allows the virtual POI associated with the "XX Airport" POI to be associated with the high-precision parking lot map to be associated. After the association is completed, when the user enters "XX Airport Parking Lot" as the navigation destination while the vehicle is traveling, the actual navigation destination on the navigation map is "XX Airport." Therefore, the high-precision parking lot map associated with the virtual POI associated with the "XX Airport Parking Lot" POI can be pre-loaded. When the navigation position reaches the vicinity of "XX Airport," navigation ends, and the vehicle can continue driving according to the high-precision parking lot map, further implementing functions such as route planning, autonomous driving, and automatic parking.

[0074] As an optional embodiment, obtaining the point of interest to be associated according to the elements of the candidate point of interest includes:

[0075] When the element condition indicates that the candidate interest points include interest point elements, the candidate interest points are determined as the interest points to be associated.

[0076] For example, Figure 3 As shown, when the candidate POI includes a POI element, the specific location of the parking lot entrance can be displayed on the navigation map. In this case, the candidate POI can be directly used as a pending POI for associating with the pending high-precision parking lot map. In this way, by pre-loading the high-precision parking lot map associated with the "underground parking lot (entrance)" POI, the vehicle can continue driving according to the high-precision parking lot map after driving to the vicinity of the "underground parking lot (entrance)" POI according to the navigation map, and further realize functions such as path planning, autonomous driving, and automatic parking.

[0077] As an optional embodiment, establishing the association relationship according to the distance between the entrance and exit location point and the point of interest to be associated includes:

[0078] When the distance is less than a preset distance threshold, associating the entrance and exit location point with the point of interest to be associated;

[0079] When the distance is greater than or equal to the preset distance threshold, a connecting road is generated on the high-precision parking lot map to be associated, and the connecting road is associated with the point of interest to be associated, wherein the connecting road is used to connect the entrance and exit location points and the point of interest to be associated.

[0080] For example, the preset distance threshold can be set based on actual conditions. For example, the preset distance threshold is 10 meters. The preset distance threshold can also be set based on the complexity of the road near the parking lot to be calibrated. For example, when the road is complex, the preset distance threshold can be set to 3 meters, and when the road is not complex, the preset distance threshold can be set to 12 meters.

[0081] For example, the accuracy of a navigation map is low, such as 10 to 30 meters. This may result in the location of a point of interest (POI) being located only near the target location, but still some distance away. High-precision maps have both absolute and relative accuracy at the centimeter level, such as 10 to 20 centimeters. When linking a navigation map with a high-precision map, it's possible that the entrance and exit locations on the high-precision parking lot map are far away from the location of the POI on the navigation map. If the distance is close, direct association is generally possible. When the user approaches the POI, they can directly observe the location of the parking lot entrance and exit near the POI, allowing them to directly determine the location of the parking lot entrance and exit associated with the POI. However, if the entrance and exit locations are far away from the location of the POI on the navigation map, it may be difficult for the user to observe the parking lot entrance and exit upon arriving near the POI. In this case, a connecting road can be generated on the high-precision parking lot map to connect the entrance and exit locations with the POI to be associated, and the connecting road can be associated with the POI to be associated. In this way, when the user drives near the associated point of interest, the connecting road can be called so that the vehicle can drive into the entrance and exit of the parking lot according to the connecting road, and the vehicle can continue to drive through the high-precision parking lot map corresponding to the parking lot.

[0082] Among them, the high-precision parking lot map includes geometric information and association relationship information of multiple layers such as the parking lot's internal roads, lanes, parking spaces, obstacles and facilities. When generating connecting roads, a new layer can be added to the high-precision parking lot map to establish association relationships, positional relationships, etc. between the high-precision parking lot map and the points of interest in the navigation map. The generated connecting roads and / or the entrance and exit locations of the high-precision map can be displayed in the newly added association layer. When the vehicle drives near the point of interest corresponding to the target location, the information of the association layer can be called through the association information of the point of interest to find the entrance and exit locations or connecting roads associated with the point of interest.

[0083] It can be understood that the connecting road is generated on the high-precision parking lot map to be associated. When the vehicle travels near the point of interest corresponding to the connecting road, it matches the connecting road, that is, matches the high-precision parking lot map to be associated, thereby realizing the linkage process between the navigation system and the automatic driving system.

[0084] As an optional embodiment, the navigation information further includes a navigation path. Before controlling the vehicle to travel using the target high-precision parking lot map, the method further includes:

[0085] Determining, based on the navigation path, a high-precision road map matching the navigation path;

[0086] According to the high-precision road map, the vehicle is controlled to travel toward the target point of interest.

[0087] For example, in scenarios where both navigation maps and HD maps are available, the navigation path can be matched with the HD map to enable fully autonomous or semi-autonomous driving of the vehicle. In actual intelligent driving applications, after the autonomous driving system obtains navigation information, it obtains the vehicle's navigation path and the target point of interest (POI) at the destination parking lot based on the navigation information. The autonomous driving system can first match the navigation path with a HD map database corresponding to the road, such as a HD map of a highway or urban road, and then control the vehicle based on the HD map to drive it toward the target POI.

[0088] As a specific example, when establishing an association relationship between a candidate point of interest and a high-precision parking map to be associated, a new association layer is added to create a logical and positional relationship between the candidate points of interest on the high-precision parking map and the navigation map. Specifically, when the distance between the candidate point of interest on the navigation map and the road on the high-precision map is within 10 meters, the candidate point of interest can be directly associated with the road at the entrance and exit location in the high-precision map, or the candidate point of interest can be directly associated with the road corresponding to the entrance and exit location in the high-precision map; if the spatial distance between the candidate point of interest on the navigation map and the nearest entrance and exit location is greater than 10 meters, a connecting road connecting the high-precision map and the navigation map can be generated in the high-precision parking map, and the starting point and end point of the connecting road are respectively the location point of the candidate point of interest on the navigation map in the high-precision map and the end point of the road corresponding to the entrance and exit location / the entrance and exit location in the high-precision parking map.

[0089] Among them, the production of connecting roads meets the requirements of high-precision maps, and is used to realize the physical connection from the parking lot entrance and exit locations on the high-precision map to the urban navigation road network, until the high-precision topology can be expanded to the location of the parking lot POI in the navigation map.

[0090] Among them, if the initial POI of the navigation map only has a simple building type and no detailed POI elements of the parking lot entrances and exits are made, virtual POI points are generated in the high-precision parking lot map to indicate the locations of the parking lot entrances and exits, and these virtual POI points can be used to establish an association between the initial POI of the navigation map and the entrance and exit locations or connecting roads of the high-precision parking lot map.

[0091] As a specific example, Figure 4 As shown in FIG, a flow chart of a vehicle navigation method is proposed. The method includes the following steps:

[0092] S401: The user searches for a parking lot POI of a certain building as a navigation destination and initiates navigation or intelligent driving.

[0093] S402: The navigation system shares the navigation path and POI with the autonomous driving system.

[0094] S403: The vehicle starts from its current location and drives onto a highway or urban road network. The autonomous driving system matches the high-precision map of the highway or urban road network based on the navigation path, and the user starts to control the vehicle or the vehicle starts the autonomous driving process.

[0095] S404. The automatic driving system retrieves a high-precision parking map associated with the POI from the high-precision map database, and loads the high-precision parking map when it is determined that the vehicle is about to enter a preset range of the POI.

[0096] S405: When the vehicle arrives near the POI, the vehicle position is matched to the parking lot entrance and exit or connecting road associated with the POI. If the match is successful, the automatic parking function is automatically initiated.

[0097] S406. The automatic parking function loads a high-precision parking map when the vehicle enters the underground parking lot, determines the target parking space as the destination based on the high-precision parking map, and plans a local path from the vehicle position to the target parking space.

[0098] S407: The automatic driving system controls the vehicle to travel along the local path planning to the target parking space and completes automatic parking.

[0099] Figure 5 FIG. 1 is a block diagram of a vehicle control device according to an exemplary embodiment. Figure 5 The vehicle control device includes an acquisition module 501, a determination module 502 and a control module 503.

[0100] An acquisition module 501 is configured to acquire navigation information of the vehicle, wherein the navigation information includes a target point of interest corresponding to a destination parking lot in a navigation map;

[0101] a determination module 502 configured to determine a target high-precision parking lot map based on the target point of interest and associations between different points of interest and different high-precision parking lot maps in the navigation map;

[0102] The control module 503 is configured to control the vehicle to travel by using the target high-precision parking lot map when the vehicle travels to the vicinity of the target point of interest.

[0103] As an optional embodiment, the vehicle control device includes a calibration module, and the calibration module includes:

[0104] A positioning acquisition module is configured to obtain positioning information of the parking lot to be calibrated;

[0105] an association determination module configured to determine, based on the positioning information, candidate points of interest near the parking lot to be calibrated in the navigation map and a high-precision parking lot map to be associated with the parking lot to be calibrated;

[0106] The establishing module is configured to establish an association relationship between the candidate point of interest and the high-precision parking lot map to be associated.

[0107] As an optional embodiment, the establishment module includes:

[0108] A first determination submodule is configured to determine the entrance and exit locations of the parking lot to be calibrated based on the high-precision parking lot map to be associated;

[0109] A second determining submodule is configured to determine a point of interest to be associated based on the elements of the candidate point of interest;

[0110] The establishing submodule is configured to establish the association relationship according to the distance between the entrance and exit location point and the point of interest to be associated.

[0111] As an optional embodiment, the second determining submodule is specifically configured as follows:

[0112] When the element condition indicates that the candidate points of interest do not include a point of interest element, generating a virtual point of interest in the high-precision parking lot map to be associated, wherein the location of the virtual point of interest is near the entrance and exit of the parking lot to be calibrated;

[0113] The virtual point of interest is associated with the candidate point of interest, and the virtual point of interest is determined as the point of interest to be associated.

[0114] As an optional embodiment, the second determining submodule is specifically configured as follows:

[0115] When the element condition indicates that the candidate interest points include interest point elements, the candidate interest points are determined as the interest points to be associated.

[0116] As an optional embodiment, the establishment submodule is specifically configured as follows:

[0117] When the distance is less than a preset distance threshold, associating the entrance and exit location point with the point of interest to be associated;

[0118] When the distance is greater than or equal to the preset distance threshold, a connecting road is generated on the high-precision parking lot map to be associated, and the connecting road is associated with the point of interest to be associated, wherein the connecting road is used to connect the entrance and exit location points and the point of interest to be associated.

[0119] As an optional embodiment, the navigation information further includes a navigation path, and the vehicle control device further includes:

[0120] A matching module is configured to determine a high-precision road map matching the navigation path based on the navigation path;

[0121] The driving control module is configured to control the vehicle to drive toward the target point of interest based on the high-precision road map.

[0122] Regarding the vehicle control device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method and will not be elaborated here.

[0123] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the vehicle control method provided by the present disclosure are implemented.

[0124] The present disclosure also provides a vehicle, comprising:

[0125] a storage device for storing a computer program;

[0126] An execution device is used to execute the computer program to implement the steps of the vehicle control method provided by the present disclosure.

[0127] The present disclosure also provides a computer program product, which includes a computer program executable by a programmable device, and has a code portion for executing the above-mentioned vehicle control method when the computer program is executed by the programmable device.

[0128] Figure 6 FIG2 is a block diagram of a vehicle 700 according to an exemplary embodiment. For example, vehicle 700 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or another type of vehicle. Vehicle 700 may be an autonomous vehicle or a semi-autonomous vehicle.

[0129] Reference Figure 6Vehicle 700 may include various subsystems, such as an infotainment system 710, a perception system 720, a decision-making control system 730, a drive system 740, and a computing platform 750. Vehicle 700 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of vehicle 700 may be interconnected via wired or wireless means.

[0130] In some embodiments, the infotainment system 710 may include a communication system, an entertainment system, a navigation system, and the like.

[0131] The perception system 720 may include several sensors for sensing information about the environment surrounding the vehicle 700. For example, the perception system 720 may include a global positioning system (which may be a GPS system, a BeiDou system, or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter-wave radar, an ultrasonic radar, and a camera.

[0132] The decision control system 730 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0133] The drive system 740 may include components that provide power to the vehicle 700. In one embodiment, the drive system 740 may include an engine, a power source, a transmission system, and wheels. The engine may be an internal combustion engine, an electric motor, an air compression engine, or a combination thereof. The engine is capable of converting energy provided by the power source into mechanical energy.

[0134] Some or all functions of the vehicle 700 are controlled by a computing platform 750. The computing platform 750 may include at least one processor 751 and a memory 752. The processor 751 may execute instructions 753 stored in the memory 752.

[0135] The processor 751 can be any conventional processor, such as a commercially available CPU. The processor can also include a graphics processor (GPU), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), or a combination thereof.

[0136] The memory 752 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0137] In addition to instructions 753 , memory 752 may also store data, such as road maps, route information, and vehicle location, direction, speed, etc. The data stored in memory 752 may be used by computing platform 750 .

[0138] In the embodiment of the present disclosure, the processor 751 can execute the instruction 753 to complete all or part of the steps of the above-mentioned vehicle control method.

[0139] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the present disclosure. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0140] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vehicle control method, characterized in that: include: Obtaining navigation information for the vehicle, the navigation information including a target point of interest corresponding to a destination parking lot in a navigation map; Determining a target high-precision parking lot map based on the target point of interest and associations between different points of interest in the navigation map and different high-precision parking lot maps; When the vehicle travels near the target point of interest, the vehicle is controlled to travel according to the target high-precision parking lot map.

2. The vehicle control method according to claim 1, characterized in that: The association relationship is obtained in the following way: Obtain the positioning information of the parking lot to be calibrated; Determining, based on the positioning information, candidate points of interest near the parking lot to be calibrated in the navigation map and a high-precision parking lot map to be associated with the parking lot to be calibrated; Establish an association relationship between the candidate points of interest and the high-precision parking lot map to be associated.

3. The vehicle control method according to claim 2, characterized in that: Establishing an association relationship between the candidate point of interest and the high-precision parking lot map to be associated includes: Determine the entrance and exit locations of the parking lot to be calibrated based on the high-precision parking lot map to be associated; Determining a point of interest to be associated based on the elements of the candidate point of interest; The association relationship is established according to the distance between the entrance and exit location point and the point of interest to be associated.

4. The vehicle control method according to claim 3, characterized in that: Determining the points of interest to be associated based on the elements of the candidate points of interest includes: When the element condition indicates that the candidate points of interest do not include a point of interest element, generating a virtual point of interest in the high-precision parking lot map to be associated, wherein the location of the virtual point of interest is near the entrance and exit of the parking lot to be calibrated; The virtual point of interest is associated with the candidate point of interest, and the virtual point of interest is determined as the point of interest to be associated.

5. The vehicle control method according to claim 3, characterized in that: According to the elements of the candidate points of interest, the points of interest to be associated are obtained, including: When the element condition indicates that the candidate interest points include interest point elements, the candidate interest points are determined as the interest points to be associated.

6. The vehicle control method according to any one of claims 3 to 5, characterized in that: Establishing the association relationship according to the distance between the entrance and exit location point and the point of interest to be associated includes: When the distance is less than a preset distance threshold, associating the entrance and exit location point with the point of interest to be associated; When the distance is greater than or equal to the preset distance threshold, a connecting road is generated on the high-precision parking lot map to be associated, and the connecting road is associated with the point of interest to be associated, wherein the connecting road is used to connect the entrance and exit location points and the point of interest to be associated.

7. The vehicle control method according to any one of claims 1 to 5, characterized in that: The navigation information also includes a navigation path. Before controlling the vehicle to travel using the target high-precision parking lot map, the method further includes: Determining, based on the navigation path, a high-precision road map matching the navigation path; According to the high-precision road map, the vehicle is controlled to travel toward the target point of interest.

8. A vehicle control device, characterized in that: include: an acquisition module configured to acquire navigation information of the vehicle, the navigation information including a target point of interest corresponding to a destination parking lot in a navigation map; a determination module configured to determine a target high-precision parking lot map based on the target point of interest and associations between different points of interest and different high-precision parking lot maps in the navigation map; The control module is configured to control the vehicle to travel by using the target high-precision parking lot map when the vehicle travels near the target point of interest.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the vehicle control method described in any one of claims 1 to 7 is implemented.

10. A vehicle, characterized in that: include: a storage device for storing a computer program; An execution device is used to execute the computer program to implement the vehicle control method described in any one of claims 1-7.

Images