Driving route recommendation method and device, equipment and storage medium

Abstract

The invention discloses a driving route recommendation method and device, equipment and a storage medium, and relates to the field of intelligent driving, and the method comprises the steps: selecting a target driving region from a map according to the driving demand of a user; a first graph layer and a second graph layer corresponding to the target driving area are determined, the first graph layer is used for reflecting the user takeover condition when the vehicle in the target driving area executes the driving assistance function, and the second graph layer is used for reflecting the usable condition of the memory driving function of the vehicle in the target driving area; and generating a recommended driving route of the user in the target driving area based on the first image layer and the second image layer. According to the method, the recommended driving route of the user in the target driving area is generated by combining the user takeover condition and the available condition of the memory driving function of the vehicle, so that early warning or avoidance of a road section where the vehicle may automatically exit the memory driving is performed in advance, and the condition that the user needs to take over the vehicle actively without signs during driving is avoided; and the driving continuity of the user is improved.

Description

Technical Field

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

[0002] Memory driving, also known as Commute Navigation on Autopilot (CNOA), is an urban-level driving assistance product. It can complete automatic assisted driving from point A to point B based on the learned specific driving route, and realize functions such as autonomous steering, lane change, obstacle avoidance, etc.

[0003] However, in the actual memory driving process, due to the complex driving scenes and changing environment, some temporary conditions may occur, such as multiple vehicles competing with each other on roads with high traffic volume, sudden road depressions on roads with normal traffic, and some roads temporarily not supporting the use of the memory driving function by vehicles. At this time, if the conventional driving route is still used, it is easy for the vehicle to exit the automatic assisted driving without warning during driving and the user to actively intervene / take over, resulting in poor driving continuity for the user, which greatly affects the user's driving experience. Therefore, the industry is in urgent need of a driving route recommendation method that can improve driving continuity during memory driving. Summary of the invention

[0004] The main purpose of the present application is to provide a driving route recommendation method, device, equipment and storage medium, aiming to provide a driving route recommendation method that can improve driving continuity during memory driving.

[0005] To achieve the above object, the present application provides a driving route recommendation method, which comprises the following steps:

[0006] Select the target driving area from the map according to the user's driving needs;

[0007] Determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect a user takeover situation when a vehicle in the target driving area performs a driving assistance function, and the second layer is used to reflect an availability situation of a memory driving function of the vehicle in the target driving area;

[0008] A recommended driving route for the user in the target driving area is generated based on the first layer and the second layer.

[0009] In one embodiment, before the step of selecting a target driving area from a map according to the user's driving needs, the step further includes:

[0010] Collecting buried point data uploaded by the target vehicle, wherein the buried point data is vehicle status data corresponding to when the target vehicle is taken over during the execution of the driving assistance function;

[0011] Visualizing the user takeover situation of the target vehicle when executing the driving assistance function based on the buried point data to obtain a takeover hotspot layer;

[0012] Among them, the part of the takeover hotspot layer corresponding to the target driving area is the first layer.

[0013] In one embodiment, the step of visualizing the user takeover situation of the target vehicle when executing the driving assistance function based on the buried point data to obtain a takeover hotspot layer includes:

[0014] Counting the user takeover frequency of the target vehicle in different road sections according to the buried data, and determining the user takeover reason of the target vehicle according to the data type of the buried data;

[0015] The user takeover situation of the target vehicle is visualized based on the user takeover frequency and the user takeover reason to obtain a takeover hotspot layer.

[0016] In one embodiment, before the step of selecting a target driving area from a map according to the user's driving needs, the step further includes:

[0017] Downloading a self-learning map and a route management list of the area where the target vehicle is located, wherein the self-learning map is used to update and store the self-learning information of the target vehicle, and the route management list contains a number of driving routes of the target vehicle;

[0018] Visualizing the self-learning confidence of each road section in the self-learning map according to the route management list to obtain a map confidence layer, wherein the self-learning confidence is used to indicate the usability of the memory driving function of the target vehicle;

[0019] Among them, the part of the map confidence layer corresponding to the target driving area is the second layer.

[0020] In one embodiment, the step of generating a recommended driving route for the user in the target driving area based on the first layer and the second layer includes:

[0021] Filtering the driving route of the user in the target driving area according to the first layer to obtain a first route;

[0022] Filtering the driving route of the user in the target driving area according to the second layer to obtain a second route;

[0023] A recommended driving route for the user in the target driving area is generated based on the first route and the second route.

[0024] In one embodiment, the driving route recommendation method further includes:

[0025] When activity recommendation information is received, determining a target user matching the activity recommendation information;

[0026] An activity recommendation route is generated in a self-learning map based on the activity recommendation information, and the activity recommendation route is pushed to the target user.

[0027] In addition, to achieve the above-mentioned purpose, the present application also proposes a driving route recommendation device, the driving route recommendation device comprising:

[0028] Demand analysis module, used to select the target driving area from the map according to the user's driving needs;

[0029] a layer acquisition module, used to determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user takeover status of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the usability status of the memory driving function of the vehicle in the target driving area;

[0030] A route recommendation module is used to generate a recommended driving route for the user in the target driving area based on the first layer and the second layer.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a driving route recommendation device, which includes: a memory, a processor, and a driving route recommendation program stored in the memory and executable on the processor, and the driving route recommendation program is configured to implement the steps of the driving route recommendation method described above.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a driving route recommendation program is stored on the storage medium. When the driving route recommendation program is executed by a processor, the steps of the driving route recommendation method described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, which includes a driving route recommendation program, and when the driving route recommendation program is executed by a processor, the steps of the driving route recommendation method described above are implemented.

[0034] This application selects a target driving area from a map according to the user's driving needs; determines a first layer and a second layer corresponding to the target driving area, the first layer is used to reflect the user's takeover of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the availability of the memory driving function of the vehicle in the target driving area; generates a recommended driving route for the user in the target driving area based on the first layer and the second layer. Compared with the traditional driving route recommendation method, the above method of the present application combines the user's takeover of the vehicle in the target driving area when the driving assistance function is executed and the availability of the memory driving function of the vehicle in the target driving area to generate the user's recommended driving route in the target driving area, thereby achieving early warning or avoiding the road sections where the vehicle may automatically exit the memory driving, avoiding the situation where the user needs to take over the vehicle without warning during driving, thereby improving the user's driving continuity. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the structure of a driving route recommendation device in a hardware operating environment involved in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the flow chart of the first embodiment of the driving route recommendation method of the present application;

[0037] Figure 3 A schematic diagram of the recommended method for taking over reminders for this application's driving route;

[0038] Figure 4 A schematic diagram of the flow chart of the second embodiment of the driving route recommendation method of the present application;

[0039] Figure 5 This is a schematic diagram of some layers that take over the hotspot layer in the driving route recommendation method of this application;

[0040] Figure 6 A schematic diagram of updating the self-learning map in the driving route recommendation method of this application;

[0041] Figure 7 A schematic diagram of some layers of the map confidence layer in the driving route recommendation method of this application;

[0042] Figure 8 A schematic diagram of a flow chart of a third embodiment of the driving route recommendation method of the present application;

[0043] Fig. 9 This is a structural block diagram of the first embodiment of the driving route recommendation device of the present application.

[0044] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a driving route recommendation device for the hardware operating environment involved in the embodiment of the present application.

[0047] like Figure 1 As shown, the driving route recommendation device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the driving route recommendation device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a driving route recommendation program.

[0050] exist Figure 1 In the driving route recommendation device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the driving route recommendation device of the present application can be set in the driving route recommendation device, and the driving route recommendation device calls the driving route recommendation program stored in the memory 1005 through the processor 1001, and executes the driving route recommendation method provided in the embodiment of the present application.

[0051] The driving route recommendation method provided in the present application can be deployed and executed on a computer device, which can be a vehicle-mounted device (such as a vehicle-mounted controller, a vehicle-mounted processor or other vehicle-mounted unit, etc.), or a terminal outside the vehicle, an independent server, a cloud, a server cluster, a distributed system, an Internet of Things device or an Internet of Vehicles device, etc. This embodiment is not limited to this, and those skilled in the art can set up a computer device that implements the driving route recommendation method according to actual needs.

[0052] The present application embodiment provides a driving route recommendation method, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the driving route recommendation method of the present application.

[0053] In this embodiment, the driving route recommendation method includes the following steps:

[0054] Step S10: Select a target driving area from the map according to the user's driving needs.

[0055] It is understandable that the above driving requirements may include but are not limited to information such as the starting point, destination, en route points, driving preferences (e.g., more spacious roads, fewer traffic lights, short driving distance, short driving time, etc.), and the above map may be an electronic map stored in the cloud.

[0056] It should be understood that the target driving area may refer to an area covered by all feasible driving routes that meet the user's driving needs in the map.

[0057] Step S20: Determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user takeover status of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the availability of the memory driving function of the vehicle in the target driving area.

[0058] It should be noted that the first layer and the second layer are both visualization layers, that is, the user can intuitively obtain the user takeover status of each road section in the target driving area and the availability of the memory driving function from the first layer and the second layer.

[0059] Among them, the user takeover situation can be obtained from the scenarios where temporary situations occur during the memory driving process of the vehicle, such as scenarios where the user frequently intervenes in the accelerator and scenarios where the user frequently intervenes in the steering wheel: on the one hand, due to the high traffic volume and complex scenarios during commuting hours, users are in a hurry to drive, so in some sections, it is easy to have multiple vehicles competing with each other, abnormal braking at intersections, and other scenarios where users frequently intervene in the accelerator; on the other hand, due to the changeable urban environment and many extreme scenarios, in some sections, it is easy to have large vehicles approaching, large curvature curves, narrow lanes, and other scenarios where users frequently intervene in the steering wheel.

[0060] The usability of the vehicle's memory driving function can be obtained from the vehicle's learning scenarios of the memory driving function in various road sections. The learning completion time is related to factors such as the route length, road conditions, and environmental complexity of each road section. For example, it takes a long time in scenes such as congestion, mixed traffic on roads, complex large intersections, roundabouts, and continuous large curvature curves. Learning may not be successful in tidal lanes, internal campus roads, and roads that have been occupied for a long time.

[0061] Step S30: generating a recommended driving route for the user in the target driving area based on the first layer and the second layer.

[0062] In a specific implementation, since the first layer reflects the user takeover of the vehicle in the target driving area when the driving assistance function is executed, the second layer reflects the availability of the vehicle's memory driving function in the target driving area. Therefore, when the user drives the vehicle in the target driving area, the vehicle-mounted system can quickly determine which sections of the road require the user to actively take over the vehicle based on the first and second layers, and make the recommended driving routes avoid these sections as much as possible while meeting the user's driving needs. In particular, for sections that cannot be avoided and require the user to actively take over the vehicle, advance warnings can be given in the recommended driving routes to remind the user to prepare for takeover in advance, thereby avoiding driving safety issues that may be caused by the user taking over without warning. For specific examples, please refer to Figure 3 , Figure 3 This is a schematic diagram of the recommended method for taking over the reminder of the driving route for this application. Figure 3 As can be seen, two warning sections are marked in the recommended driving route, where the red warning section represents a very complex section (i.e. a high takeover section), and the yellow warning section represents a relatively complex section (i.e. a higher takeover section).

[0063] This embodiment selects a target driving area from a map according to the user's driving needs; determines a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user's takeover of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the availability of the memory driving function of the vehicle in the target driving area; and generates a recommended driving route for the user in the target driving area based on the first layer and the second layer. Compared with the traditional driving route recommendation method, the above method of this embodiment combines the user's takeover of the vehicle in the target driving area when the driving assistance function is executed and the availability of the memory driving function of the vehicle in the target driving area to generate the recommended driving route for the user in the target driving area, thereby achieving early warning or avoiding the road section where the vehicle may automatically exit the memory driving, avoiding the situation where the user needs to take over the vehicle without any signs during driving, and thus improving the driving continuity of the user.

[0064] refer to Figure 4 , Figure 4 This is a flow chart of the second embodiment of the driving route recommendation method of the present application.

[0065] In a feasible implementation manner, before step S10, the following may also be included:

[0066] Step S1: Collecting buried point data uploaded by the target vehicle, wherein the buried point data is the vehicle status data corresponding to when the target vehicle is taken over during the execution of the driving assistance function.

[0067] It should be noted that the target vehicle may include all vehicles with any driving assistance function, and the vehicle status data may include but is not limited to the current location, surrounding environment, driving parameters, etc. of the target vehicle. Since the buried point data is collected in real time and continuously, the real-time and effectiveness of the hotspot layer can be guaranteed.

[0068] Among them, the above-mentioned driving assistance functions may include ACC (adaptive cruise control), ICC (intelligent cruise control), ALCA (lever lane change assist), ICC MAX (enhanced intelligent cruise control), NOA (navigation control assist), CNOA (commuter navigation control assist), etc., and this embodiment does not limit this.

[0069] Step S2: Visualize the user takeover situation of the target vehicle when executing the driving assistance function based on the buried point data to obtain a takeover hotspot layer.

[0070] Among them, the part of the takeover hotspot layer corresponding to the target driving area is the first layer.

[0071] In a specific implementation, the user takeover degree corresponding to each section of the target vehicle when executing the driving assistance function can be determined based on the above-mentioned buried data (e.g., high takeover section, relatively high takeover section, moderate takeover section, relatively low takeover section, low takeover section, etc.), and then different visualization elements (e.g., different colors, different logos, etc.) are assigned to different user takeover degrees. Figure 5 , Figure 5 This is a schematic diagram of some layers that take over the hotspot layer in the driving route recommendation method for this application. Users can Figure 5 It can be seen intuitively that the dark red section represents a high take-over section, the light red section represents a relatively high take-over section, the dark yellow section represents a moderate take-over section, the light yellow section represents a relatively low take-over section, and the green section represents a low take-over section.

[0072] In addition, for ease of understanding, the above-mentioned takeover hotspot layer can be regarded as the city's takeover hotspot total layer, and the above-mentioned first layer is a part of the layer corresponding to the target driving area related to the user's travel needs in the total layer, that is, the first layer is included in the takeover hotspot layer.

[0073] In a feasible implementation manner, step S2 may be implemented by the following steps S21 and S22:

[0074] Step S21: counting the frequency of user takeover of the target vehicle in different road sections according to the buried data, and determining the reason for user takeover of the target vehicle according to the data type of the buried data.

[0075] It should be noted that the data types of the above buried data may include but are not limited to horizontal override, vertical override, normal exit, takeover exit, user-initiated exit, and abnormal system exit. Among them, horizontal override means that the driver actively controls the direction through the steering wheel, and vertical override means that the driver actively accelerates by stepping on the accelerator / electric switch. The above takeover hotspot layer can present the takeover / intervention speed and steering wheel frequency of users in different sections when using any assisted driving function, calculate the takeover average, maximum value and other data, and drill down to analyze the reasons for the takeover. Prompt users to pay close attention on relevant sections, or temporarily close relevant routes.

[0076] For example, the user takeover reasons corresponding to the data types of the tracking point data and the required tracking points may be described based on the following Table 1. Wherein, POI (Point of Interest) represents a point of interest.

[0077] Table 1:

[0078]

[0079] Step S22: Visualizing the user takeover situation of the target vehicle based on the user takeover frequency and the user takeover reason to obtain a takeover hotspot layer.

[0080] In the specific implementation, after receiving various types of buried point data, it can be summarized and processed according to the POI information at the time of intervention / takeover, and the average and maximum values ​​of user takeovers corresponding to different sections of the road can be calculated. The algorithm selects higher / higher takeover hotspots that are higher than the average value. The selected higher / higher takeover sections are displayed in real time on the cloud map in the form of a heat map through the takeover hotspot layer. For different degrees of user takeover frequency, different colors can be used to mark them for visualization; and for different reasons for user takeover, they can be placed in different reminder boxes in the form of text for visualization, thereby obtaining the above-mentioned takeover hotspot layer.

[0081] In a feasible implementation manner, before step S10, the following may also be included:

[0082] Step S3: downloading a self-learning map and a route management list of the area where the target vehicle is located, wherein the self-learning map is used to update and store the self-learning information of the target vehicle, and the route management list contains several driving routes of the target vehicle.

[0083] It should be noted that the self-learning map is a map containing the self-learning information of the target vehicle on each road section. The self-learning information may include road information, driver lane selection / steering and other operation information, which is not limited in this embodiment.

[0084] Step S4: Visualizing the self-learning confidence of each road section in the self-learning map according to the route management list to obtain a map confidence layer, wherein the self-learning confidence is used to indicate the usability of the memory driving function of the target vehicle.

[0085] Among them, the part of the map confidence layer corresponding to the target driving area is the second layer.

[0086] In the specific implementation, you can refer to Figure 6 , Figure 6 This is a schematic diagram of updating the self-learning map in the driving route recommendation method of this application. Figure 6According to the content in the , after the vehicle is started, the CNOA map management center in the vehicle's automatic driving domain controller (ADCU, AutomatedDriving Control Unit) downloads the created self-learning map and route management list. At the same time, when it is detected that the cloud is online, the route status is synchronized to the cloud's mapping data management center for online processing. After receiving the signal, the mapping data management center checks whether the map has updated the static and dynamic multi-layer elements during multiple trips. If so, the update information is synchronized to ADCU. After processing, ADCU transmits the data to DHU (DesktopHead Unit, screen display unit), and interacts through LINK ID (a unique code used to identify a specific road or path in the map). LINK ID carries the route's topological information, lane information, and other dynamic and static layers and geometric topological layer information, converting the data into a user-readable and easy-to-understand visual language, and uniformly displays it in the learning route storage management, that is, a user-visible and operable route management card. Among them, CNOASDPathState represents the route status that needs to be updated in the self-learning map.

[0087] To more intuitively observe the availability of the vehicle memory driving function corresponding to each road segment in the map confidence layer, please refer to Figure 7 , Figure 7 This is a partial layer diagram of the map confidence layer in the driving route recommendation method of this application. Figure 7 From the perspective of the map confidence layer, the self-learning confidence corresponding to each road section can be displayed in the form of a heat map. The self-learning confidence can be in the form of a percentage to prompt the user the completion ratio of the road section learning. When the self-learning map is updated, the confidence is also dynamically updated. In the map confidence layer, the color display can be distinguished according to the status of each road section, for example, Figure 7 In the figure, blue means it has been learned, that is, the memory driving function of the target vehicle can be used on this road section; gray means it has not been learned, that is, the memory driving function of the target vehicle cannot be used on this road section. If the learning of this road section is completed after the update is completed, the road section can be changed from gray to blue.

[0088] In addition, for ease of understanding, the above-mentioned map confidence layer can also be regarded as the city's total map confidence layer, and the above-mentioned second layer is a part of the layer corresponding to the target driving area related to the user's travel needs in the total layer, that is, the second layer is included in the map confidence layer.

[0089] It should be noted that, based on the settings of the first layer and the second layer mentioned above, only part of the layers need to be loaded when generating the recommended driving route subsequently, without loading the entire takeover hotspot layer and the map confidence layer, thereby improving the loading efficiency and enabling the recommended driving route to be generated faster.

[0090] This embodiment collects the buried data uploaded by the target vehicle, and the buried data is the vehicle status data corresponding to the target vehicle when it is taken over during the execution of the driving assistance function; the user takeover frequency of the target vehicle in different sections is counted according to the buried data, and the user takeover reason of the target vehicle is determined according to the data type of the buried data; the user takeover situation of the target vehicle is visualized based on the user takeover frequency and the user takeover reason to obtain a takeover hotspot layer; wherein the portion of the takeover hotspot layer corresponding to the target driving area is the first layer; the self-learning map and route management list of the area where the target vehicle is located are downloaded, the self-learning map is used to update and store the self-learning information of the target vehicle, and the route management list contains several driving routes of the target vehicle; the self-learning confidence of each section in the self-learning map is visualized according to the route management list to obtain a map confidence layer, and the self-learning confidence is used to indicate the usability of the memory driving function of the target vehicle; wherein the portion of the map confidence layer corresponding to the target driving area is the second layer. Compared with the traditional driving route recommendation method, the above method of this embodiment introduces a takeover hotspot layer to visualize the user takeover frequency and user takeover reasons corresponding to each road section, so as to provide early prompts or avoid sections that require user active takeover, providing users with a smoother and more coherent driving experience; at the same time, this embodiment also introduces a map confidence layer to display the self-learning confidence corresponding to each road section, so as to intuitively inform users of the available and unavailable sections of the memory driving function, and clarify the available boundaries and scope of the memory driving function through data visualization, thereby improving user participation in the use of the memory driving function.

[0091] refer to Figure 8 , Figure 8 This is a flow chart of the third embodiment of the driving route recommendation method of the present application.

[0092] In a feasible implementation manner, step S30 may be implemented by the following steps S301, S302, and S303:

[0093] Step S301: screening the driving route of the user in the target driving area according to the first layer to obtain a first route.

[0094] Step S302: screening the driving route of the user in the target driving area according to the second layer to obtain a second route.

[0095] In a specific implementation, since the first layer reflects the user takeover situation of the vehicle in the target driving area when executing the driving assistance function, the high takeover sections in the target driving area can be screened out according to the first layer, and the high takeover sections can be set as non-priority recommended sections. Among them, the high takeover sections can refer to the sections whose total takeover times within a preset period (for example, 1 hour) are higher than the average takeover times of all sections in the first layer.

[0096] Similarly, since the second layer reflects the availability of the memory driving function of the vehicle in the target driving area, the road sections in the target driving area that do not support the memory driving function can be screened out according to the second layer, and the road sections that do not support the memory driving function can be set as non-priority recommended sections. Among them, the road sections that do not support the memory driving function can refer to the road sections whose self-learning confidence is lower than a preset ratio (e.g., 100%).

[0097] After filtering out high-pipe sections and sections that do not support the memory driving function in the target driving area according to the first layer and the second layer respectively, a feasible driving route can be determined according to the remaining sections, that is, the first route and the second route can be obtained.

[0098] Step S303: generating a recommended driving route for the user in the target driving area based on the first route and the second route.

[0099] In a specific implementation, the sections in the first route and the second route can be edited, opened, closed, etc. through a visual operation tool, so as to obtain a recommended driving route for the user in the target driving area. For example, a section that has been in a high takeover state for a preset time (e.g., 30 minutes) can be edited / closed, and a closed high takeover section that has been in a low takeover state for a preset time can be reopened.

[0100] In particular, if all feasible driving routes cannot avoid including the above-mentioned non-priority recommended sections (i.e., high-takeover sections and sections that do not support the memory driving function), it can be allowed to add non-priority recommended sections to the recommended driving routes. At the same time, a takeover reminder can be issued to the user in advance at the non-priority recommended sections on the map so that the user can prepare for the takeover, avoiding the situation where the user needs to actively take over the vehicle without warning during driving.

[0101] In a feasible implementation manner, the driving route recommendation method may further include:

[0102] Step S40: When activity recommendation information is received, target users matching the activity recommendation information are determined.

[0103] Step S50: generating an activity recommendation route in a self-learning map based on the activity recommendation information, and pushing the activity recommendation route to the target user.

[0104] It should be understood that the above-mentioned activity recommendation information can be information that the enterprise defines by itself and intends to recommend to the target user, such as promotion operation information, test information, etc. When the target user has the needs of fixed route testing, targeted recommendation of popular commuting routes, targeted recommendation of exclusive routes, and limited-time route recommendation of operation activities, the operator can send the activity recommendation route generated in the self-learning map based on the activity recommendation information to the display unit of the target vehicle through the autonomous driving domain controller for use by the target user, thereby realizing the boutique recommendation for the target user. For example, suppose a new 4S store is opened in a certain city. In order to promote the 4S store, the operator can push the activity recommendation route to the 4S store to all car owners in the city who have activated the CNOA function during the opening period. In addition, the operator can also upload and send the self-learning map to the user's vehicle according to the user's travel frequency, route scene difficulty, and operation activity requirements, so as to recommend more customized driving routes to users.

[0105] This embodiment filters the driving routes of the user in the target driving area according to the first layer to obtain a first route; filters the driving routes of the user in the target driving area according to the second layer to obtain a second route; generates a recommended driving route for the user in the target driving area based on the first route and the second route; when receiving activity recommendation information, determines a target user matching the activity recommendation information; generates an activity recommendation route in a self-learning map based on the activity recommendation information, and pushes the activity recommendation route to the target user. Compared with the traditional driving route recommendation method, the above method of this embodiment filters the first layer and the second layer to set the high-takeover sections and sections that do not support the memory driving function in the target driving area as non-priority recommended sections, so that the final recommended driving route can better meet the user's intelligent driving needs.

[0106] In addition, an embodiment of the present application further proposes a storage medium, on which a driving route recommendation program is stored. When the driving route recommendation program is executed by a processor, the steps of the driving route recommendation method described above are implemented.

[0107] Reference Fig. 9 , Fig. 9 This is a structural block diagram of the first embodiment of the driving route recommendation device of the present application.

[0108] like Fig. 9 As shown, the driving route recommendation device proposed in the embodiment of the present application includes:

[0109] Demand analysis module 901, used to select a target driving area from a map according to the user's driving demand;

[0110] A layer acquisition module 902 is used to determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user takeover status of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the usability status of the memory driving function of the vehicle in the target driving area;

[0111] The route recommendation module 903 is used to generate a recommended driving route for the user in the target driving area based on the first layer and the second layer.

[0112] This embodiment selects a target driving area from a map according to the user's driving needs; determines a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user's takeover of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the availability of the memory driving function of the vehicle in the target driving area; and generates a recommended driving route for the user in the target driving area based on the first layer and the second layer. Compared with the traditional driving route recommendation method, the above method of this embodiment combines the user's takeover of the vehicle in the target driving area when the driving assistance function is executed and the availability of the memory driving function of the vehicle in the target driving area to generate the recommended driving route for the user in the target driving area, thereby achieving early warning or avoiding the road section where the vehicle may automatically exit the memory driving, avoiding the situation where the user needs to take over the vehicle without any signs during driving, and thus improving the driving continuity of the user.

[0113] Based on the first embodiment of the driving route recommendation device of the present application, a second embodiment of the driving route recommendation device of the present application is proposed.

[0114] In this embodiment, the layer acquisition module 902 is also used to collect the buried point data uploaded by the target vehicle, and the buried point data is the vehicle status data corresponding to when the target vehicle is taken over during the execution of the driving assistance function; based on the buried point data, the user takeover situation of the target vehicle when executing the driving assistance function is visualized to obtain a takeover hotspot layer; wherein, the part of the takeover hotspot layer corresponding to the target driving area is the first layer.

[0115] Furthermore, the layer acquisition module 902 is also used to count the user takeover frequency of the target vehicle in different road sections according to the buried data, and determine the user takeover reason of the target vehicle according to the data type of the buried data; based on the user takeover frequency and the user takeover reason, the user takeover situation of the target vehicle is visualized to obtain a takeover hotspot layer.

[0116] Furthermore, the layer acquisition module 902 is also used to download a self-learning map and a route management list of the area where the target vehicle is located, the self-learning map is used to update and store the self-learning information of the target vehicle, and the route management list contains several driving routes of the target vehicle; the self-learning confidence of each road section in the self-learning map is visualized according to the route management list to obtain a map confidence layer, and the self-learning confidence is used to indicate the usability of the memory driving function of the target vehicle; wherein, the portion of the map confidence layer corresponding to the target driving area is the second layer.

[0117] Furthermore, the route recommendation module 903 is also used to filter the driving routes of the user in the target driving area according to the first layer to obtain a first route; filter the driving routes of the user in the target driving area according to the second layer to obtain a second route; and generate a recommended driving route for the user in the target driving area based on the first route and the second route.

[0118] Furthermore, the route recommendation module 903 is also used to determine the target user matching the activity recommendation information when the activity recommendation information is received; generate an activity recommendation route in the self-learning map based on the activity recommendation information, and push the activity recommendation route to the target user.

[0119] Other embodiments or specific implementations of the driving route recommendation device of the present application can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0120] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0121] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0123] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A driving route recommendation method, characterized in that: The method comprises the following steps: Select the target driving area from the map according to the user's driving needs; Determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect a user takeover situation when a vehicle in the target driving area performs a driving assistance function, and the second layer is used to reflect an availability situation of a memory driving function of the vehicle in the target driving area; A recommended driving route for the user in the target driving area is generated based on the first layer and the second layer.

2. The driving route recommendation method according to claim 1, characterized in that: Before the step of selecting a target driving area from a map according to the user's driving needs, the method further includes: Collecting buried point data uploaded by the target vehicle, wherein the buried point data is vehicle status data corresponding to when the target vehicle is taken over during the execution of the driving assistance function; Visualizing the user takeover situation of the target vehicle when executing the driving assistance function based on the buried point data to obtain a takeover hotspot layer; Among them, the part of the takeover hotspot layer corresponding to the target driving area is the first layer.

3. The driving route recommendation method according to claim 2, characterized in that: The step of visualizing the user takeover situation of the target vehicle when executing the driving assistance function based on the buried point data to obtain a takeover hotspot layer includes: Counting the user takeover frequency of the target vehicle in different road sections according to the buried data, and determining the user takeover reason of the target vehicle according to the data type of the buried data; The user takeover situation of the target vehicle is visualized based on the user takeover frequency and the user takeover reason to obtain a takeover hotspot layer.

4. The driving route recommendation method according to claim 3, characterized in that: Before the step of selecting a target driving area from a map according to the user's driving needs, the method further includes: Downloading a self-learning map and a route management list of the area where the target vehicle is located, wherein the self-learning map is used to update and store the self-learning information of the target vehicle, and the route management list contains a number of driving routes of the target vehicle; Visualizing the self-learning confidence of each road section in the self-learning map according to the route management list to obtain a map confidence layer, wherein the self-learning confidence is used to indicate the usability of the memory driving function of the target vehicle; Among them, the part of the map confidence layer corresponding to the target driving area is the second layer.

5. The driving route recommendation method according to claim 1, wherein: The step of generating a recommended driving route for the user in the target driving area based on the first layer and the second layer includes: Filtering the driving route of the user in the target driving area according to the first layer to obtain a first route; Filtering the driving route of the user in the target driving area according to the second layer to obtain a second route; A recommended driving route for the user in the target driving area is generated based on the first route and the second route.

6. The driving route recommendation method according to claim 1, wherein: The method further comprises: When activity recommendation information is received, determining a target user matching the activity recommendation information; An activity recommendation route is generated in a self-learning map based on the activity recommendation information, and the activity recommendation route is pushed to the target user.

7. A driving route recommendation device, characterized in that: The driving route recommendation device comprises: Demand analysis module, used to select the target driving area from the map according to the user's driving needs; a layer acquisition module, used to determine a first layer and a second layer corresponding to the target driving area, wherein the first layer is used to reflect the user takeover status of the vehicle in the target driving area when the driving assistance function is executed, and the second layer is used to reflect the usability status of the memory driving function of the vehicle in the target driving area; A route recommendation module is used to generate a recommended driving route for the user in the target driving area based on the first layer and the second layer.

8. A driving route recommendation device, characterized in that: The device comprises: a memory, a processor, and a driving route recommendation program stored in the memory and executable on the processor, wherein the driving route recommendation program is configured to implement the steps of the driving route recommendation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a driving route recommendation program is stored on the storage medium. When the driving route recommendation program is executed by a processor, the steps of the driving route recommendation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a driving route recommendation program, and when the driving route recommendation program is executed by a processor, the steps of the driving route recommendation method according to any one of claims 1 to 6 are implemented.

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