Driving route memory planning method, device, storage medium and system

Abstract

The invention provides a memory driving route planning method and device, a storage medium and a system, and the method comprises the steps: obtaining the route data of a pilot route between a starting address and a terminal address, the route data comprises the map data of a map where the pilot route is located, and the route learning state of the pilot route, the route learning state is used for representing the memory learning completion progress of the navigation route; a navigation route is displayed on the map, the navigation route is displayed at least through a first visual element and a second visual element, and the first visual element and the second visual element have visual difference; the road section corresponding to the first visual element in the piloting route is a road section which is determined according to the route learning state and can be used for memorizing driving, and the road section corresponding to the second visual element is a road section which is determined according to the route learning state and cannot be used for memorizing driving. The road section state is accurately displayed through visual element changes, so that a user can accurately know the available range of the driving memory function, and the safety of vehicle driving is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a planning method, device, storage medium and system for memorizing a driving route. Background Art

[0002] Memory driving, also known as Commute Navigation on Autopilot, CNOA (Commute Navigation on Autopilot), is an urban-level driving assistance product. Based on the learned specific route, it completes the automatic assisted driving function from the starting point to the end point, and realizes functions such as autonomous steering, lane change, obstacle avoidance, and obstacle avoidance.

[0003] Currently, before a vehicle starts memory driving, it needs to self-learn and generate a memory driving map based on multiple driving data of the vehicle on the same road section, so that the vehicle can perform automatic assisted driving according to the memory driving map. However, in current technology, the driving route in the memory driving map generated by the vehicle cannot be directly observed, and the user cannot clearly know the available range of the memory driving function. This may result in the driver being unable to take over the vehicle in time before entering a non-memory driving section, causing safety hazards. Summary of the invention

[0004] In view of this, the present disclosure provides a planning method, device, storage medium and system for memorizing a driving route, which can improve the safety of vehicle driving.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for planning a memorized driving route is provided, the method comprising:

[0006] Acquire route data of a pilot route between a start address and an end address, the route data including: map data of a map where the pilot route is located, and a route learning status of the pilot route, the route learning status being used to indicate a progress of memory learning of the pilot route;

[0007] The navigation route is displayed on the map, and the navigation route is displayed by at least a first visual element and a second visual element, and the first visual element and the second visual element have a visual difference; the section corresponding to the first visual element in the navigation route is a section that can be used for memory driving determined based on the route learning status, and the section corresponding to the second visual element is a section that cannot be used for memory driving determined based on the route learning status.

[0008] In one embodiment, before obtaining route data of the navigation route between the starting address and the ending address, the method further includes:

[0009] Acquire the driving information of the vehicle, wherein the driving information includes the starting address, the ending address and the driving time;

[0010] Determine the historical travel route according to the starting address and the end address;

[0011] Acquiring historical driving data collected by the vehicle on the historical driving route according to the driving time;

[0012] A navigation route for the vehicle is generated based on the historical driving data.

[0013] In one embodiment, the method further comprises:

[0014] Get the updated route learning status;

[0015] The navigation route displayed on the map is updated according to the updated route learning status.

[0016] In one embodiment, the first visual element is a first color, and the second visual element is a second color.

[0017] In one embodiment, the map further includes a visual element description, wherein the visual element description is used to describe the progress of the memory learning of the navigation route.

[0018] In one embodiment, the method further comprises:

[0019] During driving along the pilot route, obtaining road condition information on the pilot route;

[0020] When the road condition information is different from the historical road condition information in the map, the map data is updated according to the road condition information.

[0021] In one embodiment, when the road condition information is different from the historical road condition information in the map, updating the map data according to the road condition information includes:

[0022] According to a preset layer division rule, the road condition information is analyzed to determine a plurality of layer information corresponding to the road condition information, wherein the plurality of layers include a static layer, a dynamic layer and a topological layer, the static layer is used to determine the lane information in the pilot route, the dynamic layer is used to determine the dynamic event information in the pilot route, and the topological layer is used to determine the road structure information in the pilot route;

[0023] When any layer of the traffic condition information is different from a corresponding layer in the historical traffic condition information in the map, the corresponding layer in the map data is updated.

[0024] According to a second aspect of an embodiment of the present disclosure, a device for planning a memorized driving route is provided, the device comprising:

[0025] A first acquisition unit is used to acquire route data of a pilot route between a start address and a destination address, wherein the route data includes: map data of a map where the pilot route is located, and a route learning status of the pilot route, wherein the route learning status is used to indicate a progress of memory learning of the pilot route;

[0026] A display unit is used to display the navigation route on the map, wherein the navigation route is displayed by at least a first visual element and a second visual element, wherein the first visual element and the second visual element have a visual difference; a section corresponding to the first visual element in the navigation route is a section where memory driving can be performed according to a route learning status, and a section corresponding to the second visual element is a section where memory driving cannot be performed according to a route learning status.

[0027] In one embodiment, the apparatus further comprises:

[0028] A second acquisition unit is used to acquire the driving information of the vehicle, wherein the driving information includes a starting address, an end address and a driving time;

[0029] A determination unit, configured to determine a historical travel route according to the starting address and the end address;

[0030] A third acquisition unit, configured to acquire historical driving data collected by the vehicle in the historical driving route according to the driving time;

[0031] A generating unit is used to generate a navigation route for the vehicle based on the historical driving data.

[0032] In one embodiment, the device further comprises:

[0033] A fourth acquisition unit, used to acquire an updated route learning status;

[0034] The first updating unit is used to update the navigation route displayed on the map according to the updated route learning status.

[0035] In one embodiment, the first visual element is a first color, and the second visual element is a second color.

[0036] In one embodiment, the map further includes a visual element description, and the visual element description is used to describe the progress of memory learning of the navigation route.

[0037] In one embodiment, the apparatus further comprises:

[0038] A fifth acquisition unit, used for acquiring road condition information on the navigation route during driving along the navigation route;

[0039] The second updating unit is used to update the map data according to the road condition information when the road condition information is different from the historical road condition information in the map.

[0040] In one embodiment, the second updating unit is used to:

[0041] According to a preset layer division rule, the road condition information is analyzed to determine a plurality of layer information corresponding to the road condition information, wherein the plurality of layers include a static layer, a dynamic layer and a topological layer, the static layer is used to determine the lane information in the pilot route, the dynamic layer is used to determine the dynamic event information in the pilot route, and the topological layer is used to determine the road structure information in the pilot route;

[0042] When any layer of the traffic condition information is different from a corresponding layer in the historical traffic condition information in the map, the corresponding layer in the map data is updated.

[0043] According to a third aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any method described in the first aspect are implemented.

[0044] According to a fourth aspect of an embodiment of the present disclosure, a planning system for memorizing a driving route is provided, comprising a vehicle and a cloud server, wherein the system is used to execute the steps of any method described in the first aspect above.

[0045] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0046] By visualizing the navigation section of memory driving and accurately displaying the status of the section through changes in visual elements, users can accurately know the available scope of the memory driving function, thereby improving the safety of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a method for planning a memorized driving route shown in an exemplary embodiment of the present disclosure;

[0048] Figure 2 is a flow chart of a method for generating a navigation route shown in an exemplary embodiment of the present disclosure;

[0049] Figure 3 is a flow chart of a method for updating map data shown in an exemplary embodiment of the present disclosure;

[0050] Figure 4 is a flow chart of another method for updating map data shown in an exemplary embodiment of the present disclosure;

[0051] Figure 5 is a vehicle system structure diagram showing an exemplary embodiment of the present disclosure;

[0052] Figure 6 is a flow chart of another method for planning a memorized driving route according to an exemplary embodiment of the present disclosure;

[0053] Figure 7 It is a block diagram of a planning device for memorizing a driving route shown in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0055] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0056] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0057] Memory driving technology is a technology that can complete the automatic assisted driving function from the starting point to the end point based on the specific route learned by the vehicle. Before the memory driving begins, the vehicle can build a self-learning map based on the historical driving information on the same section of road, and save the self-learning map to the vehicle memory after the map is built. The next time the user enters the starting point and end point of the section, the vehicle can download the self-learning map in the memory and determine the driving route, driving speed and other information from the starting point to the end point based on the self-learning map, thereby realizing the memory driving function.

[0058] However, the time it takes for a vehicle to build and download a self-learning map is related to factors such as route length, road conditions, and environmental complexity. For example, it takes a long time to build a self-learning map in driving scenarios with heavy loads such as congestion, mixed traffic, complex large intersections, roundabouts, and continuous large curvature curves. However, it may not be possible to successfully build a self-learning map on sections such as tidal lanes, internal roads in parks, and roads that have been occupied for a long time. Therefore, it takes a certain amount of time for a vehicle to build a self-learning map. In addition, the construction process of the self-learning map is constructed in sections, that is, after the vehicle learns and memorizes, there will be some continuous sections that can be released to users for use. However, at present, users cannot accurately know the route learning results and route availability in the self-learning map, so that users cannot clearly know the available range of the memory driving function, which may result in the driver being unable to take over the vehicle in time before entering the non-memory driving section, causing safety hazards.

[0059] In addition, because the road environment in the city changes rapidly, if learning is not continued on the saved self-learning map, the self-learning map cannot be updated, resulting in a certain lag in the self-learning map, resulting in low accuracy of the self-learning map, and thus leading to low vehicle memory driving safety.

[0060] Based on this, the present disclosure proposes a method for planning a memorized driving route, which can be applied to a vehicle system. For details, please refer to Figure 1 The flowchart shown includes the following steps:

[0061] S101, obtaining route data of a navigation route between a starting address and an end address.

[0062] In some embodiments, after the vehicle is ignited, the user enters the starting address and the end address in the display device in the vehicle, and the vehicle downloads the route data of the pilot route between the starting address and the end address in the map management center of the vehicle CNOA (Commute Navigation on Autopilot, an automatic driving navigation system) according to the starting address and the end address. The route data includes: map data of the map where the pilot route is located, and the route learning status of the pilot route, and the route learning status is used to indicate the completion progress of the memory learning of the pilot route. Among them, the pilot route is generated based on the historical driving data of the vehicle between the starting address and the end address, and is used to guide the vehicle to perform the memory driving task. Through the pilot route, the vehicle can determine the driving route between the starting address and the end address, as well as the lane information, dynamic event information and road structure information in the driving route, as well as the vehicle's driving speed, driving habits and other information. The route learning status of the pilot route refers to the completion progress of the pilot route in the process of downloading map data or building a self-learning map. For example, the distance from point A to point B is 10 kilometers, and the memory learning of the current navigation route has completed 2 kilometers. The route learning status may include the 2-kilometer learned section and the 8-kilometer unfinished section.

[0063] In one embodiment, the user inputs the starting address as point A and the end address as point B, and the vehicle can download the map data of the self-learning map that has been created before in the map management center of CNOA. The self-learning map is marked with a list of pilot routes between point A and point B. There can be multiple pilot routes in the pilot route list, each of which is generated according to different historical driving data and corresponds to different time periods. The user can determine the route data of the pilot route for this memory driving according to the pilot route list.

[0064] S102: Display the navigation route on the map.

[0065] In some embodiments, the pilot route is displayed at least by a first visual element and a second visual element, and the first visual element and the second visual element have visual differences; the road section corresponding to the first visual element in the pilot route is a road section that can be driven by memory determined according to the route learning state, and the road section corresponding to the second visual element is a road section that cannot be driven by memory determined according to the route learning state. The road section that can be driven by memory is a road section that has completed memory learning or has completed the self-learning map as indicated in the route learning state of the pilot route, and conversely, the road section that cannot be driven by memory is a road section that has not completed memory learning or has not completed the self-learning map as indicated in the route learning state of the pilot route. The first visual element refers to a visual feature used to indicate a road section that can be driven by memory, such as color, shape, transparency, etc. The second visual element refers to a visual feature used to indicate a road section that cannot be driven by memory. Specifically, a heat map can also be used to display the route learning state of the pilot route. The map is a map after the self-learning map and the high-precision map are merged.

[0066] In addition, when displaying the navigation route on the map, in addition to displaying the sections of the navigation route where memory driving is possible and the sections of the navigation route where memory driving is not possible through the first visual element and the second visual element, the map may also include a visual element description, which describes the progress of memory learning of the navigation route.

[0067] In one embodiment, the first visual element may be a first color, and the second visual element may be a second color. That is, the first color indicates the road section in the pilot route where memory driving can be performed, and the second color indicates the road section in the pilot route where memory driving cannot be performed. For example, the first color is blue, and the second color is gray. There is a distance of 16 kilometers between the starting address and the end address, of which the first 8 kilometers have completed memory learning, so blue can be used to indicate that the road section of these 8 kilometers can be memory driven. Correspondingly, the road section of the last 8 kilometers has not completed memory learning, so gray can be used to indicate that the road section of these 8 kilometers cannot be memory driven. Then the corresponding visual element description is the description of the blue and gray road sections. Specifically, a text box can be added to the map, and the text box can be used to explain that "the blue road section is the road section where memory driving can be performed, and the gray road section is the road section where memory driving cannot be performed", and the length information of the road section in the pilot route where memory driving can be performed can be displayed in the text box, for example, the road section where memory driving can be performed is 8 kilometers.

[0068] Since the self-learning map showing the pilot route is loaded in real time, and the process of constructing the pilot route is carried out in sections, it is necessary to obtain the learning status of the pilot route in real time, and update the visual elements of the pilot route on the map according to the updated route learning status. For example, if the route learning status of the pilot route indicates that the road section that can be used for memory driving increases the length of the overall pilot route by 10%, then it is necessary to update the road section of 10% length from the previous second visual element to the first visual element in the map. For example, the gray color that was previously used to indicate that the road section of 10% length could not be used for memory driving is updated to the blue color that can be used for memory driving.

[0069] In some embodiments, before obtaining the route data of the pilot route between the starting address and the end address, it is necessary to first construct a map and store the map data storing the pilot route in the memory of the vehicle. The process of generating the pilot route is described below, please refer to Figure 2 The flowchart shown specifically includes the following steps:

[0070] S201, obtaining vehicle driving information.

[0071] S202: Determine a historical driving route based on the starting address and the ending address.

[0072] S203: Acquire historical driving data collected by the vehicle in the historical driving route according to the driving time.

[0073] S204: Generate a navigation route for the vehicle based on the historical driving data.

[0074] In some embodiments, the driving information includes a starting address, an end address, and a driving time. According to the starting address and the end address, the historical driving route of the vehicle can be determined. There may be multiple driving routes between the same starting address and the end address, and it is necessary to determine the historical driving route that the vehicle has traveled in the multiple driving routes. Further combined with the driving time, the historical driving data collected when the vehicle enters the historical driving route during the time period is obtained to determine the basic route conditions of the historical route. For example, some routes may be congested during peak hours in the morning and evening, and it is necessary to slow down; or there may be a large number of pedestrians on the road section passing through the school during school hours or after school hours. The way in which the vehicle collects historical driving data can be to collect road condition information in the historical driving route through the vehicle sensor, for example, to collect road images through visual sensors, determine basic lane information, the setting and removal of dynamic events in the road, etc. For another example, the point cloud data collected during the driving process by laser radar is used to determine the road takeover relationship, connection relationship, curvature update and other information. According to the historical driving data collected by the vehicle, the self-learning mapping process of memory driving can be carried out to generate a self-learning map including the vehicle navigation route.

[0075] It should be noted that in order to save computing resources on the vehicle side, after collecting driving data, the vehicle can upload the driving data to the cloud server, call the vehicle's historical driving data through the cloud server, and build a self-learning map based on the historical driving data. The map information is then sent to the vehicle so that the vehicle can display the navigation route.

[0076] In order to improve the timeliness and accuracy of map data and provide users with a better driving experience, it is necessary to update and optimize the already constructed map data in real time. Through multiple updates, when a vehicle enters the same route multiple times, the map data information of multiple trips is merged, the invalid old elements in the map data are removed, and the valid new elements are updated to generate a complete and real-time union map data. At the same time, in order to improve the user experience, this disclosure uses the silent update method to update the map data. Please refer to Figure 3 The flowchart shown specifically includes the following steps:

[0077] S301, while the vehicle is traveling along the navigation route, obtaining road condition information on the navigation route.

[0078] S302: When the road condition information is different from the historical road condition information in the map, update the map data according to the road condition information.

[0079] In some embodiments, when the vehicle is traveling along the pilot route, the current road condition information of the pilot route can be obtained through the vehicle sensor. Then, the current road condition information is compared with the historical road condition information used when building the map to see if they are the same. If the current road condition information is the same as the historical road condition information used when building the map, there is no need to update the map data. If the current road condition information is different from the historical road condition information used when building the map, the map data needs to be updated according to the current road condition information.

[0080] In some embodiments, the process of updating map data is shown in Figure 4 The flowchart shown includes the following steps:

[0081] S401, analyzing the road condition information according to a preset layer division rule to determine a plurality of layer information corresponding to the road condition information.

[0082] S402: When any layer of the road condition information is different from a corresponding layer in the historical road condition information in the map, the corresponding layer in the map data is updated.

[0083] The multiple layers include static layers, dynamic layers and topological layers. The static layers are used to determine the lane information in the pilot route, the dynamic layers are used to determine the dynamic event information in the pilot route, and the topological layers are used to determine the road structure information in the pilot route. Among them, the lane information includes: intersections, road edges, lane line colors, lane line types, special guidance, lane arrows and other information, and may also include information such as signs and traffic lights in the lanes. Dynamic event information includes: construction, accidents, traffic control and other information. Road structure information includes: road topology, road curvature and other information.

[0084] According to the preset layer division rules, the data information included in each layer can be determined, and combined with the current road condition information, the data in the road condition information can be split to obtain the static layer, dynamic layer and topology layer corresponding to the road condition information. Among them, the static layer pays more attention to data information such as lane line changes (color, type, geometric changes), intersection changes (type, element changes), lane attribute updates (time restrictions, special lanes, ramp entrances and exits). The dynamic layer pays more attention to the setting and removal of various dynamic events on the road. The topology layer pays more attention to information such as road takeover relationships, connection relationships, and curvature updates.

[0085] After obtaining the layer corresponding to the current traffic condition information, compare the current traffic condition information with the multiple layers of the historical traffic condition information to see if they are the same. For example, compare whether the static layer corresponding to the current traffic condition information is the same as the static layer of the historical traffic condition information, compare whether the dynamic layer corresponding to the current traffic condition information is the same as the dynamic layer of the historical traffic condition information, and compare whether the topological layer corresponding to the current traffic condition information is the same as the topological layer of the historical traffic condition information. If it is determined that any layer of the current traffic condition information is different from the layer corresponding to the historical traffic condition information, update the layer in the map data. For example, if the static layer corresponding to the current traffic condition information is different from the static layer of the historical traffic condition information, update the static layer in the map data.

[0086] By comparing and updating the road condition information layer by layer, the map data can be updated seamlessly, and the vehicle can perform the task of memory driving according to the latest map data, thereby improving the accuracy and safety of the vehicle's memory driving.

[0087] In one embodiment, the silent update process of the map data can be completed in the cloud server. The process of the planning method for memorizing the driving route is described in general below.

[0088] like Figure 5 The vehicle system structure diagram shown in the figure includes a map display device 510, a domain controller 520 and a cloud server 530. The map display device 510 is used to display map data and route data of the navigation route to the user. The domain controller 520 is used to load map data to the map display device, and the domain controller includes a memory for storing the created map data. The cloud server 530 is used to manage the historical driving data used to create the map data, and update the map data according to the road condition information in the historical driving data and the current road condition information.

[0089] Lieutenant General Figure 6 The flowchart shown further illustrates the planning method of memorizing the driving route.

[0090] S601 , the user inputs the starting address and the ending address of this driving in the map display device 510 .

[0091] S602, the domain controller 520 downloads map data and a route list between the starting address and the ending address from the CNOA map management center according to the starting address and the ending address.

[0092] S603, the domain controller 520 transmits the map data and the route list to the map display device 510, and the user selects the navigation route for this driving.

[0093] S604, the map display device 510 obtains the navigation route of this driving from the domain controller 520, and displays the navigation route on the map through the first visual element and the second visual element.

[0094] S605, the vehicle performs the memory driving task according to the navigation route, obtains the road condition information collected during driving, and transmits it to the cloud server 530.

[0095] S606, the cloud server 530 determines through the mapping data management center whether the current traffic information is the same as the historical traffic information used to construct the map.

[0096] S607: If the current traffic condition information is the same as the historical traffic condition information used to construct the map, the map data does not need to be updated; if the current traffic condition information is different from the historical traffic condition information used to construct the map, the map data is updated according to the current traffic condition information.

[0097] S608 , the cloud server 530 transmits the updated map data to the domain controller 520 .

[0098] S609, the domain controller 520 transmits the map data to the map display component 510 by carrying the dynamic and static layers including the route topology information, lane information, and the topology layer information LINK ID, so that the map display component 510 displays the updated map data.

[0099] Corresponding to the above-mentioned embodiment of the method for planning a driving route memory, the present disclosure also provides an embodiment of a device for planning a driving route memory.

[0100] Please refer to Figure 7 A block diagram of a planning device for memorizing a driving route is shown, the device comprising:

[0101] A first acquisition unit 701 is used to acquire route data of a pilot route between a start address and a destination address, wherein the route data includes: map data of a map where the pilot route is located, and a route learning status of the pilot route, wherein the route learning status is used to indicate a progress of memory learning of the pilot route;

[0102] The display unit 702 is used to display the navigation route on the map, and the navigation route is displayed by at least a first visual element and a second visual element, and the first visual element and the second visual element have a visual difference; the section corresponding to the first visual element in the navigation route is a section that can be used for memory driving determined based on the route learning status, and the section corresponding to the second visual element is a section that cannot be used for memory driving determined based on the route learning status.

[0103] In one embodiment, the apparatus further comprises:

[0104] A second acquisition unit is used to acquire the driving information of the vehicle, wherein the driving information includes a starting address, an end address and a driving time;

[0105] A determination unit, configured to determine a historical travel route according to the starting address and the end address;

[0106] A third acquisition unit, configured to acquire historical driving data collected by the vehicle in the historical driving route according to the driving time;

[0107] A generating unit is used to generate a navigation route for the vehicle based on the historical driving data.

[0108] In one embodiment, the device further comprises:

[0109] A fourth acquisition unit, used to acquire an updated route learning status;

[0110] The first updating unit is used to update the navigation route displayed on the map according to the updated route learning status.

[0111] In one embodiment, the first visual element is a first color, and the second visual element is a second color.

[0112] In one embodiment, the map further includes a visual element description, and the visual element description is used to describe the progress of memory learning of the navigation route.

[0113] In one embodiment, the apparatus further comprises:

[0114] A fifth acquisition unit, used for acquiring road condition information on the navigation route during driving along the navigation route;

[0115] The second updating unit is used to update the map data according to the road condition information when the road condition information is different from the historical road condition information in the map.

[0116] In one embodiment, the second updating unit is used to:

[0117] According to a preset layer division rule, the road condition information is analyzed to determine a plurality of layer information corresponding to the road condition information, wherein the plurality of layers include a static layer, a dynamic layer and a topological layer, the static layer is used to determine the lane information in the pilot route, the dynamic layer is used to determine the dynamic event information in the pilot route, and the topological layer is used to determine the road structure information in the pilot route;

[0118] When any layer of the traffic condition information is different from a corresponding layer in the historical traffic condition information in the map, the corresponding layer in the map data is updated.

[0119] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0120] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0121] Based on the same inventive concept, the present disclosure also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, any step of the planning method for memorizing a driving route is implemented.

[0122] Based on the same inventive concept, the present disclosure also provides a planning system for memorizing a driving route, including a vehicle and a cloud server, and the system is used to implement any step of the planning method for memorizing a driving route.

[0123] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0124] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method for planning a driving route memory, characterized in that: The method comprises: Acquire route data of a pilot route between a start address and an end address, the route data including: map data of a map where the pilot route is located, and a route learning status of the pilot route, the route learning status being used to indicate a progress of memory learning of the pilot route; The navigation route is displayed on the map, and the navigation route is displayed by at least a first visual element and a second visual element, and the first visual element and the second visual element have a visual difference; the section corresponding to the first visual element in the navigation route is a section that can be used for memory driving determined based on the route learning status, and the section corresponding to the second visual element is a section that cannot be used for memory driving determined based on the route learning status.

2. The method according to claim 1, characterized in that Before obtaining the route data of the pilot route between the starting address and the end address, the method further includes: Acquire the driving information of the vehicle, wherein the driving information includes the starting address, the ending address and the driving time; Determine the historical travel route according to the starting address and the end address; Acquiring historical driving data collected by the vehicle on the historical driving route according to the driving time; A navigation route for the vehicle is generated based on the historical driving data.

3. The method according to claim 1, characterized in that The method further comprises: Get the updated route learning status; The navigation route displayed on the map is updated according to the updated route learning status.

4. The method according to claim 1, characterized in that: The first visual element is a first color, and the second visual element is a second color.

5. The method according to claim 1, characterized in that The map also includes a visual element description, which is used to describe the progress of memory learning of the navigation route.

6. The method according to claim 1, characterized in that The method further comprises: When the vehicle is traveling along the navigation route, obtaining road condition information on the navigation route; When the road condition information is different from the historical road condition information in the map, the map data is updated according to the road condition information.

7. The method according to claim 6, characterized in that When the road condition information is different from the historical road condition information in the map, updating the map data according to the road condition information includes: According to a preset layer division rule, the road condition information is analyzed to determine a plurality of layer information corresponding to the road condition information, wherein the plurality of layers include a static layer, a dynamic layer and a topological layer, the static layer is used to determine the lane information in the pilot route, the dynamic layer is used to determine the dynamic event information in the pilot route, and the topological layer is used to determine the road structure information in the pilot route; When any layer of the traffic condition information is different from a layer corresponding to the historical traffic condition information in the map, the corresponding layer in the map data is updated.

8. A driving route memory planning device, characterized in that: The device comprises: A first acquisition unit is used to acquire route data of a pilot route between a start address and a destination address, wherein the route data includes: map data of a map where the pilot route is located, and a route learning status of the pilot route, wherein the route learning status is used to indicate a progress of memory learning of the pilot route; A display unit is used to display the navigation route on the map, wherein the navigation route is displayed by at least a first visual element and a second visual element, wherein the first visual element and the second visual element have a visual difference; a section corresponding to the first visual element in the navigation route is a section where memory driving can be performed according to a route learning status, and a section corresponding to the second visual element is a section where memory driving cannot be performed according to a route learning status.

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

10. A system for planning a driving route memory, comprising a vehicle and a cloud server, characterized in that: The system is used to execute the steps of any one of the methods of claims 1 to 7.

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